{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dbronnikov07/Adidas-Sales-Prediction/blob/main/Adidas_demand_Forecasting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "25ffa3094c4b49558b81257dd89ff780",
        "deepnote_cell_type": "text-cell-h1",
        "id": "m688-DooId6w"
      },
      "source": [
        "# Чистка Данных"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "f5b66146",
        "execution_start": 1708811820079,
        "execution_millis": 1042,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "83dee2a36ebf4f08964caab2728ac0b7",
        "deepnote_cell_type": "code",
        "id": "x7h-jnPdId6y"
      },
      "source": [
        "import datetime\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "99af7e25",
        "execution_start": 1708811821125,
        "execution_millis": 200,
        "deepnote_to_be_reexecuted": false,
        "deepnote_output_height_limit_disabled": true,
        "cell_id": "e52cc382458c43fb9a7e70e4d3d3c0a2",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 719
        },
        "id": "Je9PdlyIId6z",
        "outputId": "355024fa-2efe-401a-9f4c-e91d266a1116"
      },
      "source": [
        "df = pd.read_csv(\"Adidas_Another version.csv\", sep=\";\")\n",
        "data = df\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Retailer  Retailer ID Invoice Date     Region     State      City  \\\n",
              "0  Foot Locker    1185732.0   01.01.2020  Northeast  New York  New York   \n",
              "1  Foot Locker    1185732.0   02.01.2020  Northeast  New York  New York   \n",
              "2  Foot Locker    1185732.0   03.01.2020  Northeast  New York  New York   \n",
              "3  Foot Locker    1185732.0   04.01.2020  Northeast  New York  New York   \n",
              "4  Foot Locker    1185732.0   05.01.2020  Northeast  New York  New York   \n",
              "5  Foot Locker    1185732.0   06.01.2020  Northeast  New York  New York   \n",
              "6  Foot Locker    1185732.0   07.01.2020  Northeast  New York  New York   \n",
              "7  Foot Locker    1185732.0   08.01.2020  Northeast  New York  New York   \n",
              "8  Foot Locker    1185732.0   21.01.2020  Northeast  New York  New York   \n",
              "9  Foot Locker    1185732.0   22.01.2020  Northeast  New York  New York   \n",
              "\n",
              "                     Product Price per Unit Units Sold Total Sales  \\\n",
              "0      Men's Street Footwear        $50,00       1 200    $60 000    \n",
              "1    Men's Athletic Footwear        $50,00       1 000    $50 000    \n",
              "2    Women's Street Footwear        $40,00       1 000    $40 000    \n",
              "3  Women's Athletic Footwear        $45,00         850    $38 250    \n",
              "4              Men's Apparel        $60,00         900    $54 000    \n",
              "5            Women's Apparel        $50,00       1 000    $50 000    \n",
              "6      Men's Street Footwear        $50,00       1 250    $62 500    \n",
              "7    Men's Athletic Footwear        $50,00         900    $45 000    \n",
              "8    Women's Street Footwear        $40,00         950    $38 000    \n",
              "9  Women's Athletic Footwear        $45,00         825    $37 125    \n",
              "\n",
              "  Operating Profit Operating Margin Sales Method  Unnamed: 13  \n",
              "0        $300 000               50%     In-store          NaN  \n",
              "1        $150 000               30%     In-store          NaN  \n",
              "2        $140 000               35%     In-store          NaN  \n",
              "3        $133 875               35%     In-store          NaN  \n",
              "4        $162 000               30%     In-store          NaN  \n",
              "5        $125 000               25%     In-store          NaN  \n",
              "6        $312 500               50%     In-store          NaN  \n",
              "7        $135 000               30%       Outlet          NaN  \n",
              "8        $133 000               35%       Outlet          NaN  \n",
              "9        $129 938               35%       Outlet          NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe91a666-41f7-4351-8063-2fb4ea9c5377\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Retailer</th>\n",
              "      <th>Retailer ID</th>\n",
              "      <th>Invoice Date</th>\n",
              "      <th>Region</th>\n",
              "      <th>State</th>\n",
              "      <th>City</th>\n",
              "      <th>Product</th>\n",
              "      <th>Price per Unit</th>\n",
              "      <th>Units Sold</th>\n",
              "      <th>Total Sales</th>\n",
              "      <th>Operating Profit</th>\n",
              "      <th>Operating Margin</th>\n",
              "      <th>Sales Method</th>\n",
              "      <th>Unnamed: 13</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>1185732.0</td>\n",
              "      <td>01.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>Men's Street Footwear</td>\n",
              "      <td>$50,00</td>\n",
              "      <td>1 200</td>\n",
              "      <td>$60 000</td>\n",
              "      <td>$300 000</td>\n",
              "      <td>50%</td>\n",
              "      <td>In-store</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>1185732.0</td>\n",
              "      <td>02.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>Men's Athletic Footwear</td>\n",
              "      <td>$50,00</td>\n",
              "      <td>1 000</td>\n",
              "      <td>$50 000</td>\n",
              "      <td>$150 000</td>\n",
              "      <td>30%</td>\n",
              "      <td>In-store</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>1185732.0</td>\n",
              "      <td>03.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>Women's Street Footwear</td>\n",
              "      <td>$40,00</td>\n",
              "      <td>1 000</td>\n",
              "      <td>$40 000</td>\n",
              "      <td>$140 000</td>\n",
              "      <td>35%</td>\n",
              "      <td>In-store</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>1185732.0</td>\n",
              "      <td>04.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>Women's Athletic Footwear</td>\n",
              "      <td>$45,00</td>\n",
              "      <td>850</td>\n",
              "      <td>$38 250</td>\n",
              "      <td>$133 875</td>\n",
              "      <td>35%</td>\n",
              "      <td>In-store</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>1185732.0</td>\n",
              "      <td>05.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>Men's Apparel</td>\n",
              "      <td>$60,00</td>\n",
              "      <td>900</td>\n",
              "      <td>$54 000</td>\n",
              "      <td>$162 000</td>\n",
              "      <td>30%</td>\n",
              "      <td>In-store</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>1185732.0</td>\n",
              "      <td>06.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>Women's Apparel</td>\n",
              "      <td>$50,00</td>\n",
              "      <td>1 000</td>\n",
              "      <td>$50 000</td>\n",
              "      <td>$125 000</td>\n",
              "      <td>25%</td>\n",
              "      <td>In-store</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>1185732.0</td>\n",
              "      <td>07.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>Men's Street Footwear</td>\n",
              "      <td>$50,00</td>\n",
              "      <td>1 250</td>\n",
              "      <td>$62 500</td>\n",
              "      <td>$312 500</td>\n",
              "      <td>50%</td>\n",
              "      <td>In-store</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>1185732.0</td>\n",
              "      <td>08.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>Men's Athletic Footwear</td>\n",
              "      <td>$50,00</td>\n",
              "      <td>900</td>\n",
              "      <td>$45 000</td>\n",
              "      <td>$135 000</td>\n",
              "      <td>30%</td>\n",
              "      <td>Outlet</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>1185732.0</td>\n",
              "      <td>21.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>Women's Street Footwear</td>\n",
              "      <td>$40,00</td>\n",
              "      <td>950</td>\n",
              "      <td>$38 000</td>\n",
              "      <td>$133 000</td>\n",
              "      <td>35%</td>\n",
              "      <td>Outlet</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>1185732.0</td>\n",
              "      <td>22.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>New York</td>\n",
              "      <td>New York</td>\n",
              "      <td>Women's Athletic Footwear</td>\n",
              "      <td>$45,00</td>\n",
              "      <td>825</td>\n",
              "      <td>$37 125</td>\n",
              "      <td>$129 938</td>\n",
              "      <td>35%</td>\n",
              "      <td>Outlet</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe91a666-41f7-4351-8063-2fb4ea9c5377')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fe91a666-41f7-4351-8063-2fb4ea9c5377 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fe91a666-41f7-4351-8063-2fb4ea9c5377');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b11dab87-3fd0-439d-8eae-b155fc04b45f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b11dab87-3fd0-439d-8eae-b155fc04b45f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b11dab87-3fd0-439d-8eae-b155fc04b45f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "repr_error": "'str' object has no attribute 'empty'"
            }
          },
          "metadata": {},
          "execution_count": 373
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "e2c79671",
        "execution_start": 1708811821331,
        "execution_millis": 311,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "d4d01fee3ac64de689a3dad4170a1de5",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "UWX6R24LId6z",
        "outputId": "8b2bebe0-eb76-4c54-e009-bce1ac200583"
      },
      "source": [
        "df = data\n",
        "print(df.columns)\n",
        "df = df.drop(columns=['Retailer ID', 'Units Sold', 'Operating Profit', 'Operating Margin'], axis=1)\n",
        "\n",
        "# Оставляем retailer, date, region, product, total sales (прогнозируем), sales method\n",
        "df = df.drop(columns=['State', 'City'])\n",
        "df = df.drop('Unnamed: 13', axis=1)\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Retailer', 'Retailer ID', 'Invoice Date', 'Region', 'State', 'City',\n",
            "       'Product', 'Price per Unit', 'Units Sold', 'Total Sales',\n",
            "       'Operating Profit', 'Operating Margin', 'Sales Method', 'Unnamed: 13'],\n",
            "      dtype='object')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Retailer Invoice Date     Region                    Product  \\\n",
              "0  Foot Locker   01.01.2020  Northeast      Men's Street Footwear   \n",
              "1  Foot Locker   02.01.2020  Northeast    Men's Athletic Footwear   \n",
              "2  Foot Locker   03.01.2020  Northeast    Women's Street Footwear   \n",
              "3  Foot Locker   04.01.2020  Northeast  Women's Athletic Footwear   \n",
              "4  Foot Locker   05.01.2020  Northeast              Men's Apparel   \n",
              "5  Foot Locker   06.01.2020  Northeast            Women's Apparel   \n",
              "6  Foot Locker   07.01.2020  Northeast      Men's Street Footwear   \n",
              "7  Foot Locker   08.01.2020  Northeast    Men's Athletic Footwear   \n",
              "8  Foot Locker   21.01.2020  Northeast    Women's Street Footwear   \n",
              "9  Foot Locker   22.01.2020  Northeast  Women's Athletic Footwear   \n",
              "\n",
              "  Price per Unit Total Sales Sales Method  \n",
              "0        $50,00     $60 000      In-store  \n",
              "1        $50,00     $50 000      In-store  \n",
              "2        $40,00     $40 000      In-store  \n",
              "3        $45,00     $38 250      In-store  \n",
              "4        $60,00     $54 000      In-store  \n",
              "5        $50,00     $50 000      In-store  \n",
              "6        $50,00     $62 500      In-store  \n",
              "7        $50,00     $45 000        Outlet  \n",
              "8        $40,00     $38 000        Outlet  \n",
              "9        $45,00     $37 125        Outlet  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-70c43a4a-251a-4724-907f-6fb7b3848468\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Retailer</th>\n",
              "      <th>Invoice Date</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product</th>\n",
              "      <th>Price per Unit</th>\n",
              "      <th>Total Sales</th>\n",
              "      <th>Sales Method</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>01.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Street Footwear</td>\n",
              "      <td>$50,00</td>\n",
              "      <td>$60 000</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>02.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Athletic Footwear</td>\n",
              "      <td>$50,00</td>\n",
              "      <td>$50 000</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>03.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Street Footwear</td>\n",
              "      <td>$40,00</td>\n",
              "      <td>$40 000</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>04.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Athletic Footwear</td>\n",
              "      <td>$45,00</td>\n",
              "      <td>$38 250</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>05.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Apparel</td>\n",
              "      <td>$60,00</td>\n",
              "      <td>$54 000</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>06.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Apparel</td>\n",
              "      <td>$50,00</td>\n",
              "      <td>$50 000</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>07.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Street Footwear</td>\n",
              "      <td>$50,00</td>\n",
              "      <td>$62 500</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>08.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Athletic Footwear</td>\n",
              "      <td>$50,00</td>\n",
              "      <td>$45 000</td>\n",
              "      <td>Outlet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>21.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Street Footwear</td>\n",
              "      <td>$40,00</td>\n",
              "      <td>$38 000</td>\n",
              "      <td>Outlet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>22.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Athletic Footwear</td>\n",
              "      <td>$45,00</td>\n",
              "      <td>$37 125</td>\n",
              "      <td>Outlet</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70c43a4a-251a-4724-907f-6fb7b3848468')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-70c43a4a-251a-4724-907f-6fb7b3848468 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-70c43a4a-251a-4724-907f-6fb7b3848468');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-97482fa6-625c-45f6-baf1-5f84384e2460\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-97482fa6-625c-45f6-baf1-5f84384e2460')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-97482fa6-625c-45f6-baf1-5f84384e2460 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "repr_error": "'str' object has no attribute 'empty'"
            }
          },
          "metadata": {},
          "execution_count": 374
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "ad13a82a",
        "execution_start": 1708811821368,
        "execution_millis": 274,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "bd7a9437a6b541ed88af4509b8033140",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "QnZs9MPrId60",
        "outputId": "09128f76-7c4f-4676-c55b-a9081793ec68"
      },
      "source": [
        "mask_dollar = lambda x: float(str(x).replace(\"$\", \"\").replace(u\"\\xa0\", \"\").replace(\",\", \".\").replace(\" \", \"\"))\n",
        "df[\"Total Sales\"] = df[\"Total Sales\"].apply(mask_dollar)\n",
        "df[\"Price per Unit\"] = df[\"Price per Unit\"].apply(mask_dollar)\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Retailer Invoice Date     Region                    Product  \\\n",
              "0  Foot Locker   01.01.2020  Northeast      Men's Street Footwear   \n",
              "1  Foot Locker   02.01.2020  Northeast    Men's Athletic Footwear   \n",
              "2  Foot Locker   03.01.2020  Northeast    Women's Street Footwear   \n",
              "3  Foot Locker   04.01.2020  Northeast  Women's Athletic Footwear   \n",
              "4  Foot Locker   05.01.2020  Northeast              Men's Apparel   \n",
              "5  Foot Locker   06.01.2020  Northeast            Women's Apparel   \n",
              "6  Foot Locker   07.01.2020  Northeast      Men's Street Footwear   \n",
              "7  Foot Locker   08.01.2020  Northeast    Men's Athletic Footwear   \n",
              "8  Foot Locker   21.01.2020  Northeast    Women's Street Footwear   \n",
              "9  Foot Locker   22.01.2020  Northeast  Women's Athletic Footwear   \n",
              "\n",
              "   Price per Unit  Total Sales Sales Method  \n",
              "0            50.0      60000.0     In-store  \n",
              "1            50.0      50000.0     In-store  \n",
              "2            40.0      40000.0     In-store  \n",
              "3            45.0      38250.0     In-store  \n",
              "4            60.0      54000.0     In-store  \n",
              "5            50.0      50000.0     In-store  \n",
              "6            50.0      62500.0     In-store  \n",
              "7            50.0      45000.0       Outlet  \n",
              "8            40.0      38000.0       Outlet  \n",
              "9            45.0      37125.0       Outlet  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8318cb83-f840-4cb9-8b40-f9d71e90dc8f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Retailer</th>\n",
              "      <th>Invoice Date</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product</th>\n",
              "      <th>Price per Unit</th>\n",
              "      <th>Total Sales</th>\n",
              "      <th>Sales Method</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>01.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Street Footwear</td>\n",
              "      <td>50.0</td>\n",
              "      <td>60000.0</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>02.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Athletic Footwear</td>\n",
              "      <td>50.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>03.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Street Footwear</td>\n",
              "      <td>40.0</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>04.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Athletic Footwear</td>\n",
              "      <td>45.0</td>\n",
              "      <td>38250.0</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>05.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Apparel</td>\n",
              "      <td>60.0</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>06.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Apparel</td>\n",
              "      <td>50.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>07.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Street Footwear</td>\n",
              "      <td>50.0</td>\n",
              "      <td>62500.0</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>08.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Athletic Footwear</td>\n",
              "      <td>50.0</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>Outlet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>21.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Street Footwear</td>\n",
              "      <td>40.0</td>\n",
              "      <td>38000.0</td>\n",
              "      <td>Outlet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>22.01.2020</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Athletic Footwear</td>\n",
              "      <td>45.0</td>\n",
              "      <td>37125.0</td>\n",
              "      <td>Outlet</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8318cb83-f840-4cb9-8b40-f9d71e90dc8f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8318cb83-f840-4cb9-8b40-f9d71e90dc8f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8318cb83-f840-4cb9-8b40-f9d71e90dc8f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-57f2cb6d-5d2a-4192-8116-969ab07d9c42\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-57f2cb6d-5d2a-4192-8116-969ab07d9c42')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-57f2cb6d-5d2a-4192-8116-969ab07d9c42 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "repr_error": "'str' object has no attribute 'empty'"
            }
          },
          "metadata": {},
          "execution_count": 375
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "af59ad51",
        "execution_start": 1708811821411,
        "execution_millis": 231,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "2338a8fbce8b4b83901ecc0ecc742801",
        "deepnote_cell_type": "code",
        "id": "pBYqbnaXId60"
      },
      "source": [
        "df =df.drop(\"Invoice Date\", axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "737653a0",
        "execution_start": 1708811821412,
        "execution_millis": 231,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "2ce8bac7c1774e09933e1355a8125af7",
        "deepnote_cell_type": "code",
        "id": "C0oMf7M1Id60"
      },
      "source": [
        "data_forgrap = df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "67e70fc9",
        "execution_start": 1708811821465,
        "execution_millis": 528,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "f7cecb5588b5451dbd7e7e97fb05dde4",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "SSIZVWnNId60",
        "outputId": "b4c1e184-5420-4798-b4bc-d9a2d5b0019f"
      },
      "source": [
        "df = pd.get_dummies(df, columns=[\"Region\", \"Retailer\", \"Sales Method\", \"Product\"], dtype=int)\n",
        "# for column in [\"Region\", \"Retailer\", \"Sales Method\", \"Product\"]:\n",
        "    #df[column] = pd.factorize(df[column])[0]\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Price per Unit  Total Sales  Region_Midwest  Region_Northeast  \\\n",
              "0            50.0      60000.0               0                 1   \n",
              "1            50.0      50000.0               0                 1   \n",
              "2            40.0      40000.0               0                 1   \n",
              "3            45.0      38250.0               0                 1   \n",
              "4            60.0      54000.0               0                 1   \n",
              "5            50.0      50000.0               0                 1   \n",
              "6            50.0      62500.0               0                 1   \n",
              "7            50.0      45000.0               0                 1   \n",
              "8            40.0      38000.0               0                 1   \n",
              "9            45.0      37125.0               0                 1   \n",
              "\n",
              "   Region_South  Region_Southeast  Region_West  Retailer_Amazon  \\\n",
              "0             0                 0            0                0   \n",
              "1             0                 0            0                0   \n",
              "2             0                 0            0                0   \n",
              "3             0                 0            0                0   \n",
              "4             0                 0            0                0   \n",
              "5             0                 0            0                0   \n",
              "6             0                 0            0                0   \n",
              "7             0                 0            0                0   \n",
              "8             0                 0            0                0   \n",
              "9             0                 0            0                0   \n",
              "\n",
              "   Retailer_Foot Locker  Retailer_Kohl's  ...  Retailer_West Gear  \\\n",
              "0                     1                0  ...                   0   \n",
              "1                     1                0  ...                   0   \n",
              "2                     1                0  ...                   0   \n",
              "3                     1                0  ...                   0   \n",
              "4                     1                0  ...                   0   \n",
              "5                     1                0  ...                   0   \n",
              "6                     1                0  ...                   0   \n",
              "7                     1                0  ...                   0   \n",
              "8                     1                0  ...                   0   \n",
              "9                     1                0  ...                   0   \n",
              "\n",
              "   Sales Method_In-store  Sales Method_Online  Sales Method_Outlet  \\\n",
              "0                      1                    0                    0   \n",
              "1                      1                    0                    0   \n",
              "2                      1                    0                    0   \n",
              "3                      1                    0                    0   \n",
              "4                      1                    0                    0   \n",
              "5                      1                    0                    0   \n",
              "6                      1                    0                    0   \n",
              "7                      0                    0                    1   \n",
              "8                      0                    0                    1   \n",
              "9                      0                    0                    1   \n",
              "\n",
              "   Product_Men's Apparel  Product_Men's Athletic Footwear  \\\n",
              "0                      0                                0   \n",
              "1                      0                                1   \n",
              "2                      0                                0   \n",
              "3                      0                                0   \n",
              "4                      1                                0   \n",
              "5                      0                                0   \n",
              "6                      0                                0   \n",
              "7                      0                                1   \n",
              "8                      0                                0   \n",
              "9                      0                                0   \n",
              "\n",
              "   Product_Men's Street Footwear  Product_Women's Apparel  \\\n",
              "0                              1                        0   \n",
              "1                              0                        0   \n",
              "2                              0                        0   \n",
              "3                              0                        0   \n",
              "4                              0                        0   \n",
              "5                              0                        1   \n",
              "6                              1                        0   \n",
              "7                              0                        0   \n",
              "8                              0                        0   \n",
              "9                              0                        0   \n",
              "\n",
              "   Product_Women's Athletic Footwear  Product_Women's Street Footwear  \n",
              "0                                  0                                0  \n",
              "1                                  0                                0  \n",
              "2                                  0                                1  \n",
              "3                                  1                                0  \n",
              "4                                  0                                0  \n",
              "5                                  0                                0  \n",
              "6                                  0                                0  \n",
              "7                                  0                                0  \n",
              "8                                  0                                1  \n",
              "9                                  1                                0  \n",
              "\n",
              "[10 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-97909ed6-47be-4009-b366-b28adc80aacc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price per Unit</th>\n",
              "      <th>Total Sales</th>\n",
              "      <th>Region_Midwest</th>\n",
              "      <th>Region_Northeast</th>\n",
              "      <th>Region_South</th>\n",
              "      <th>Region_Southeast</th>\n",
              "      <th>Region_West</th>\n",
              "      <th>Retailer_Amazon</th>\n",
              "      <th>Retailer_Foot Locker</th>\n",
              "      <th>Retailer_Kohl's</th>\n",
              "      <th>...</th>\n",
              "      <th>Retailer_West Gear</th>\n",
              "      <th>Sales Method_In-store</th>\n",
              "      <th>Sales Method_Online</th>\n",
              "      <th>Sales Method_Outlet</th>\n",
              "      <th>Product_Men's Apparel</th>\n",
              "      <th>Product_Men's Athletic Footwear</th>\n",
              "      <th>Product_Men's Street Footwear</th>\n",
              "      <th>Product_Women's Apparel</th>\n",
              "      <th>Product_Women's Athletic Footwear</th>\n",
              "      <th>Product_Women's Street Footwear</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50.0</td>\n",
              "      <td>60000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>40.0</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45.0</td>\n",
              "      <td>38250.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60.0</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>50.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>50.0</td>\n",
              "      <td>62500.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>50.0</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>40.0</td>\n",
              "      <td>38000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>45.0</td>\n",
              "      <td>37125.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 22 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97909ed6-47be-4009-b366-b28adc80aacc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-97909ed6-47be-4009-b366-b28adc80aacc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-97909ed6-47be-4009-b366-b28adc80aacc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-92ca8a76-651e-4f90-9d19-78903ffb47a6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-92ca8a76-651e-4f90-9d19-78903ffb47a6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-92ca8a76-651e-4f90-9d19-78903ffb47a6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 378
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "131e818a",
        "execution_start": 1708811821499,
        "execution_millis": 494,
        "deepnote_table_state": {
          "sortBy": [],
          "filters": [],
          "pageSize": 5,
          "pageIndex": 1930
        },
        "deepnote_table_loading": false,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "894840ac6f5442418473cbc4f3f71bb0",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "shjUtSfYId61",
        "outputId": "af31f7a4-2261-46e7-f5e4-e32382e279de"
      },
      "source": [
        "df2 = df.copy()\n",
        "\n",
        "#df2 = df2.drop(\"Price per Unit\", axis=1)\n",
        "\n",
        "df2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Price per Unit  Total Sales  Region_Midwest  Region_Northeast  \\\n",
              "0               50.0      60000.0               0                 1   \n",
              "1               50.0      50000.0               0                 1   \n",
              "2               40.0      40000.0               0                 1   \n",
              "3               45.0      38250.0               0                 1   \n",
              "4               60.0      54000.0               0                 1   \n",
              "...              ...          ...             ...               ...   \n",
              "9647            29.0       2407.0               0                 1   \n",
              "9648             NaN          NaN               0                 0   \n",
              "9649             NaN          NaN               0                 0   \n",
              "9650             NaN          NaN               0                 0   \n",
              "9651             NaN          NaN               0                 0   \n",
              "\n",
              "      Region_South  Region_Southeast  Region_West  Retailer_Amazon  \\\n",
              "0                0                 0            0                0   \n",
              "1                0                 0            0                0   \n",
              "2                0                 0            0                0   \n",
              "3                0                 0            0                0   \n",
              "4                0                 0            0                0   \n",
              "...            ...               ...          ...              ...   \n",
              "9647             0                 0            0                0   \n",
              "9648             0                 0            0                0   \n",
              "9649             0                 0            0                0   \n",
              "9650             0                 0            0                0   \n",
              "9651             0                 0            0                0   \n",
              "\n",
              "      Retailer_Foot Locker  Retailer_Kohl's  ...  Retailer_West Gear  \\\n",
              "0                        1                0  ...                   0   \n",
              "1                        1                0  ...                   0   \n",
              "2                        1                0  ...                   0   \n",
              "3                        1                0  ...                   0   \n",
              "4                        1                0  ...                   0   \n",
              "...                    ...              ...  ...                 ...   \n",
              "9647                     1                0  ...                   0   \n",
              "9648                     0                0  ...                   0   \n",
              "9649                     0                0  ...                   0   \n",
              "9650                     0                0  ...                   0   \n",
              "9651                     0                0  ...                   0   \n",
              "\n",
              "      Sales Method_In-store  Sales Method_Online  Sales Method_Outlet  \\\n",
              "0                         1                    0                    0   \n",
              "1                         1                    0                    0   \n",
              "2                         1                    0                    0   \n",
              "3                         1                    0                    0   \n",
              "4                         1                    0                    0   \n",
              "...                     ...                  ...                  ...   \n",
              "9647                      0                    0                    1   \n",
              "9648                      0                    0                    0   \n",
              "9649                      0                    0                    0   \n",
              "9650                      0                    0                    0   \n",
              "9651                      0                    0                    0   \n",
              "\n",
              "      Product_Men's Apparel  Product_Men's Athletic Footwear  \\\n",
              "0                         0                                0   \n",
              "1                         0                                1   \n",
              "2                         0                                0   \n",
              "3                         0                                0   \n",
              "4                         1                                0   \n",
              "...                     ...                              ...   \n",
              "9647                      0                                0   \n",
              "9648                      0                                0   \n",
              "9649                      0                                0   \n",
              "9650                      0                                0   \n",
              "9651                      0                                0   \n",
              "\n",
              "      Product_Men's Street Footwear  Product_Women's Apparel  \\\n",
              "0                                 1                        0   \n",
              "1                                 0                        0   \n",
              "2                                 0                        0   \n",
              "3                                 0                        0   \n",
              "4                                 0                        0   \n",
              "...                             ...                      ...   \n",
              "9647                              0                        0   \n",
              "9648                              0                        0   \n",
              "9649                              0                        0   \n",
              "9650                              0                        0   \n",
              "9651                              0                        0   \n",
              "\n",
              "      Product_Women's Athletic Footwear  Product_Women's Street Footwear  \n",
              "0                                     0                                0  \n",
              "1                                     0                                0  \n",
              "2                                     0                                1  \n",
              "3                                     1                                0  \n",
              "4                                     0                                0  \n",
              "...                                 ...                              ...  \n",
              "9647                                  0                                1  \n",
              "9648                                  0                                0  \n",
              "9649                                  0                                0  \n",
              "9650                                  0                                0  \n",
              "9651                                  0                                0  \n",
              "\n",
              "[9652 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7407db18-3502-4481-868c-947ed5bcddc2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price per Unit</th>\n",
              "      <th>Total Sales</th>\n",
              "      <th>Region_Midwest</th>\n",
              "      <th>Region_Northeast</th>\n",
              "      <th>Region_South</th>\n",
              "      <th>Region_Southeast</th>\n",
              "      <th>Region_West</th>\n",
              "      <th>Retailer_Amazon</th>\n",
              "      <th>Retailer_Foot Locker</th>\n",
              "      <th>Retailer_Kohl's</th>\n",
              "      <th>...</th>\n",
              "      <th>Retailer_West Gear</th>\n",
              "      <th>Sales Method_In-store</th>\n",
              "      <th>Sales Method_Online</th>\n",
              "      <th>Sales Method_Outlet</th>\n",
              "      <th>Product_Men's Apparel</th>\n",
              "      <th>Product_Men's Athletic Footwear</th>\n",
              "      <th>Product_Men's Street Footwear</th>\n",
              "      <th>Product_Women's Apparel</th>\n",
              "      <th>Product_Women's Athletic Footwear</th>\n",
              "      <th>Product_Women's Street Footwear</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50.0</td>\n",
              "      <td>60000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>40.0</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45.0</td>\n",
              "      <td>38250.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60.0</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9647</th>\n",
              "      <td>29.0</td>\n",
              "      <td>2407.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9648</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9649</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9650</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9651</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9652 rows × 22 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7407db18-3502-4481-868c-947ed5bcddc2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7407db18-3502-4481-868c-947ed5bcddc2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7407db18-3502-4481-868c-947ed5bcddc2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8fa14393-0287-4306-ac3a-8c41420981fc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8fa14393-0287-4306-ac3a-8c41420981fc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8fa14393-0287-4306-ac3a-8c41420981fc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2"
            }
          },
          "metadata": {},
          "execution_count": 379
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "ba2fa6a5",
        "execution_start": 1708811821572,
        "execution_millis": 421,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "dcd2b471ae094a159eb323a49f62a523",
        "deepnote_cell_type": "code",
        "id": "SvgWZRLyId61"
      },
      "source": [
        "df2 =df2.drop([9651], axis=0)\n",
        "data_forgrap =data_forgrap.drop([9651], axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "8654b451",
        "execution_start": 1708811821605,
        "execution_millis": 388,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "15c3c539565c4e30a06c535ffe73ac3e",
        "deepnote_cell_type": "code",
        "id": "C16BpzLSId61"
      },
      "source": [
        "df2 =df2.drop([9650], axis=0)\n",
        "data_forgrap =data_forgrap.drop([9650], axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "66981e54",
        "execution_start": 1708811821606,
        "execution_millis": 387,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "9b1757d2c84a41239a148d954317fdca",
        "deepnote_cell_type": "code",
        "id": "KqTSW2FdId62"
      },
      "source": [
        "df2 =df2.drop([9649], axis=0)\n",
        "data_forgrap =data_forgrap.drop([9649], axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "a64e2ad",
        "execution_start": 1708811821607,
        "execution_millis": 386,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "2ebd2ecf5f404eae91045a3e00ee6d72",
        "deepnote_cell_type": "code",
        "id": "GCqbvJP5Id62"
      },
      "source": [
        "df2 =df2.drop([9648], axis=0)\n",
        "data_forgrap =data_forgrap.drop([9648], axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "9b6f1cc0",
        "execution_start": 1708811821607,
        "execution_millis": 387,
        "deepnote_to_be_reexecuted": false,
        "deepnote_output_height_limit_disabled": true,
        "cell_id": "14e6bd3f7e314bd498c480093bb0e716",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6I6dn78Id62",
        "outputId": "18358d0d-2386-4b9e-9205-c76c7dd9af60"
      },
      "source": [
        "df2.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Price per Unit                       0\n",
              "Total Sales                          0\n",
              "Region_Midwest                       0\n",
              "Region_Northeast                     0\n",
              "Region_South                         0\n",
              "Region_Southeast                     0\n",
              "Region_West                          0\n",
              "Retailer_Amazon                      0\n",
              "Retailer_Foot Locker                 0\n",
              "Retailer_Kohl's                      0\n",
              "Retailer_Sports Direct               0\n",
              "Retailer_Walmart                     0\n",
              "Retailer_West Gear                   0\n",
              "Sales Method_In-store                0\n",
              "Sales Method_Online                  0\n",
              "Sales Method_Outlet                  0\n",
              "Product_Men's Apparel                0\n",
              "Product_Men's Athletic Footwear      0\n",
              "Product_Men's Street Footwear        0\n",
              "Product_Women's Apparel              0\n",
              "Product_Women's Athletic Footwear    0\n",
              "Product_Women's Street Footwear      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 384
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "b709a9ce",
        "execution_start": 1708811821712,
        "execution_millis": 576,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "c9653812bff34e638eee7b1609c8a8f6",
        "deepnote_cell_type": "code",
        "id": "pm1kHVYtId62"
      },
      "source": [
        "df2 = df2.sample(frac=1).reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "8244b7710e8647e598955ed65cc05b5c",
        "deepnote_cell_type": "text-cell-h1",
        "id": "dYUUZRGNId62"
      },
      "source": [
        "# ГРАФИКИ ИЗНАЧАЛЬНЫХ ДАННЫХ"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "1f4f19ad",
        "execution_start": 1708811821713,
        "execution_millis": 575,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "e475b5e1a9fb48d6b142477772efc74d",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "PLWYUq4EId62",
        "outputId": "f0a1a35c-b502-4bac-fc9c-da43085c026c"
      },
      "source": [
        "data_forgrap"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Retailer     Region                    Product  Price per Unit  \\\n",
              "0     Foot Locker  Northeast      Men's Street Footwear            50.0   \n",
              "1     Foot Locker  Northeast    Men's Athletic Footwear            50.0   \n",
              "2     Foot Locker  Northeast    Women's Street Footwear            40.0   \n",
              "3     Foot Locker  Northeast  Women's Athletic Footwear            45.0   \n",
              "4     Foot Locker  Northeast              Men's Apparel            60.0   \n",
              "...           ...        ...                        ...             ...   \n",
              "9643  Foot Locker  Northeast              Men's Apparel            50.0   \n",
              "9644  Foot Locker  Northeast            Women's Apparel            41.0   \n",
              "9645  Foot Locker  Northeast      Men's Street Footwear            41.0   \n",
              "9646  Foot Locker  Northeast    Men's Athletic Footwear            42.0   \n",
              "9647  Foot Locker  Northeast    Women's Street Footwear            29.0   \n",
              "\n",
              "      Total Sales Sales Method  \n",
              "0         60000.0     In-store  \n",
              "1         50000.0     In-store  \n",
              "2         40000.0     In-store  \n",
              "3         38250.0     In-store  \n",
              "4         54000.0     In-store  \n",
              "...           ...          ...  \n",
              "9643       3200.0       Outlet  \n",
              "9644       4305.0       Outlet  \n",
              "9645       7544.0       Outlet  \n",
              "9646       2940.0       Outlet  \n",
              "9647       2407.0       Outlet  \n",
              "\n",
              "[9648 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f2aa3568-8809-409f-a107-6078fe5fa0a3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Retailer</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product</th>\n",
              "      <th>Price per Unit</th>\n",
              "      <th>Total Sales</th>\n",
              "      <th>Sales Method</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Street Footwear</td>\n",
              "      <td>50.0</td>\n",
              "      <td>60000.0</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Athletic Footwear</td>\n",
              "      <td>50.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Street Footwear</td>\n",
              "      <td>40.0</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Athletic Footwear</td>\n",
              "      <td>45.0</td>\n",
              "      <td>38250.0</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Apparel</td>\n",
              "      <td>60.0</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>In-store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9643</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Apparel</td>\n",
              "      <td>50.0</td>\n",
              "      <td>3200.0</td>\n",
              "      <td>Outlet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9644</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Apparel</td>\n",
              "      <td>41.0</td>\n",
              "      <td>4305.0</td>\n",
              "      <td>Outlet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9645</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Street Footwear</td>\n",
              "      <td>41.0</td>\n",
              "      <td>7544.0</td>\n",
              "      <td>Outlet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9646</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Men's Athletic Footwear</td>\n",
              "      <td>42.0</td>\n",
              "      <td>2940.0</td>\n",
              "      <td>Outlet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9647</th>\n",
              "      <td>Foot Locker</td>\n",
              "      <td>Northeast</td>\n",
              "      <td>Women's Street Footwear</td>\n",
              "      <td>29.0</td>\n",
              "      <td>2407.0</td>\n",
              "      <td>Outlet</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9648 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2aa3568-8809-409f-a107-6078fe5fa0a3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f2aa3568-8809-409f-a107-6078fe5fa0a3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f2aa3568-8809-409f-a107-6078fe5fa0a3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a2c1a960-c372-45ec-9796-f5ace9bc8df9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a2c1a960-c372-45ec-9796-f5ace9bc8df9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a2c1a960-c372-45ec-9796-f5ace9bc8df9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_forgrap",
              "summary": "{\n  \"name\": \"data_forgrap\",\n  \"rows\": 9648,\n  \"fields\": [\n    {\n      \"column\": \"Retailer\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Foot Locker\",\n          \"Walmart\",\n          \"Amazon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"South\",\n          \"Southeast\",\n          \"West\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Men's Street Footwear\",\n          \"Men's Athletic Footwear\",\n          \"Women's Apparel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price per Unit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.705397125008332,\n        \"min\": 7.0,\n        \"max\": 110.0,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          66.0,\n          36.0,\n          76.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12716.392111457199,\n        \"min\": 0.0,\n        \"max\": 82500.0,\n        \"num_unique_values\": 3082,\n        \"samples\": [\n          7735.0,\n          2125.0,\n          1479.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales Method\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"In-store\",\n          \"Outlet\",\n          \"Online\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 386
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "2fb96e35",
        "execution_start": 1708811821723,
        "execution_millis": 565,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "b11048c468da4529a6a299ff14b7a8b5",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "PvXv_BIqId63",
        "outputId": "49b96f73-66c3-43f6-dd92-f2d0a0242d9d"
      },
      "source": [
        "data_forgrap['Price per Unit'].plot(kind='hist',bins=25,figsize=(12,7),title='Products price distribution',color=\"#FB8500\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Products price distribution'}, ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 387
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "e237fe22",
        "execution_start": 1708811822157,
        "execution_millis": 480,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "681797b075a54f988b51c608f1e03fb5",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "hHxZs8f9Id63",
        "outputId": "4b718d04-cafb-4716-dd37-5a15fddfbc88"
      },
      "source": [
        "#top_products= data_forgrap[['Price per Unit','Total Sales']].groupby('Price per Unit').mean()\n",
        "#top_products.head(10)\n",
        "#top_products.plot(kind='bar', figsize=(12,7),title='Total sales per Price', color=\"#FB8500\")\n",
        "#plt.show()\n",
        "#top_products= data_forgrap[['Price per Unit','Total Sales']].groupby('Price per Unit')\n",
        "#top_products.head(10)\n",
        "#top_products.describe()\n",
        "\n",
        "top_products = data_forgrap.groupby(pd.cut(data_forgrap['Price per Unit'], np.arange(0, 101, 10)))[\"Total Sales\"].mean()\n",
        "top_products.plot(kind='bar', figsize=(12,7),title='Total sales per Price', color=\"#FB8500\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "f72131b3",
        "execution_start": 1708811822487,
        "execution_millis": 452,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "a16af7d3a7c54d6b9e962a6b71dfc345",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "yiolwnXsId63",
        "outputId": "a90cc423-f26c-4c9d-9d4b-afa16ac792c8"
      },
      "source": [
        "\n",
        "top_products= data_forgrap[['Product','Total Sales']].groupby('Product').mean()\n",
        "top_products.plot(kind='bar',figsize=(12,7),title='Mean sales per Product', color=\"#FB8500\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "658ea67e",
        "execution_start": 1708811822823,
        "execution_millis": 331,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "022ed64e20d04637ae0c3a29af4c1fa2",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "MWlCBeF3Id64",
        "outputId": "ffdcd107-a843-496d-8e32-217c7bc881f1"
      },
      "source": [
        "top_salesmt= data_forgrap[['Sales Method','Total Sales']].groupby('Sales Method').mean()\n",
        "top_salesmt.plot(kind='barh',title='Top Selling Methods', color=\"#FB8500\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Top Selling Methods'}, ylabel='Sales Method'>"
            ]
          },
          "metadata": {},
          "execution_count": 390
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x1700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "e522b96c",
        "execution_start": 1708811823125,
        "execution_millis": 237,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "52fcb9469715477a8edf77dff6854fe7",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "Jdlm6c8CId64",
        "outputId": "92f07f89-94b0-466e-cef4-3838ceaac1bf"
      },
      "source": [
        "top_retailers= data_forgrap[['Retailer','Total Sales']].groupby('Retailer').mean()\n",
        "\n",
        "#top_retailers.plot(kind='pie',subplots=True,figsize=(12,7),title='Total sales per Retailer',autopct='%1.0f%%')\n",
        "\n",
        "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
        "plt.pie(top_retailers[\"Total Sales\"], labels = top_retailers.index, colors = sns.color_palette(\"flare\"), autopct='%.0f%%')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "606bb464",
        "execution_start": 1708811823371,
        "execution_millis": 387,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "8e269518e4a24fa3a436827a14b1f495",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "id": "1niiSZ-GId64",
        "outputId": "64522844-ec80-4b99-add8-52a765edabcb"
      },
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "corr = df2.corr()\n",
        "sns.heatmap(corr, cmap=sns.color_palette(\"flare\"), vmin=-1, center=0, square=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 392
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "ea509b59",
        "execution_start": 1708811824364,
        "execution_millis": 434,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "2de0838a57c948e2bcdc4a2979d7ff7d",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "IeyiBlDQId64",
        "outputId": "f9f7e682-d1bf-48f6-918d-606f69790db2"
      },
      "source": [
        "import plotly.express as px\n",
        "fig = px.histogram(df2, x=\"Price per Unit\", title=\"Distribution of PPU\")\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"1688bb91-31e6-4f54-9010-f71488f37f50\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"1688bb91-31e6-4f54-9010-f71488f37f50\")) {                    Plotly.newPlot(                        \"1688bb91-31e6-4f54-9010-f71488f37f50\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"Price per Unit=%{x}\\u003cbr\\u003ecount=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[35.0,25.0,61.0,43.0,55.0,56.0,60.0,38.0,47.0,55.0,40.0,45.0,20.0,65.0,45.0,37.0,39.0,15.0,18.0,24.0,75.0,50.0,39.0,27.0,42.0,47.0,28.0,50.0,30.0,55.0,50.0,49.0,33.0,45.0,53.0,50.0,44.0,49.0,18.0,44.0,50.0,53.0,30.0,51.0,29.0,40.0,23.0,35.0,53.0,45.0,39.0,34.0,48.0,51.0,18.0,77.0,60.0,42.0,62.0,65.0,38.0,38.0,42.0,38.0,55.0,74.0,40.0,58.0,55.0,55.0,39.0,80.0,47.0,45.0,13.0,51.0,20.0,60.0,45.0,41.0,65.0,25.0,30.0,30.0,75.0,47.0,51.0,39.0,43.0,44.0,95.0,34.0,55.0,63.0,35.0,75.0,35.0,38.0,38.0,41.0,35.0,51.0,41.0,47.0,50.0,48.0,15.0,39.0,50.0,76.0,48.0,28.0,37.0,30.0,30.0,35.0,42.0,50.0,50.0,49.0,42.0,75.0,29.0,30.0,40.0,32.0,45.0,44.0,27.0,51.0,12.0,48.0,47.0,32.0,64.0,25.0,69.0,35.0,69.0,38.0,47.0,19.0,53.0,55.0,36.0,41.0,58.0,25.0,95.0,46.0,40.0,15.0,33.0,38.0,37.0,50.0,60.0,47.0,40.0,37.0,55.0,48.0,60.0,28.0,43.0,50.0,65.0,50.0,37.0,48.0,50.0,60.0,40.0,51.0,72.0,35.0,20.0,65.0,53.0,50.0,33.0,75.0,70.0,79.0,41.0,20.0,45.0,75.0,60.0,33.0,29.0,41.0,50.0,54.0,68.0,45.0,14.0,23.0,50.0,38.0,32.0,49.0,43.0,33.0,38.0,20.0,33.0,36.0,60.0,26.0,30.0,32.0,58.0,45.0,25.0,28.0,64.0,64.0,43.0,34.0,30.0,10.0,50.0,36.0,77.0,35.0,35.0,26.0,70.0,46.0,40.0,16.0,39.0,55.0,22.0,30.0,40.0,30.0,52.0,55.0,95.0,32.0,41.0,37.0,34.0,16.0,41.0,39.0,25.0,50.0,41.0,66.0,35.0,45.0,19.0,59.0,15.0,75.0,31.0,43.0,37.0,45.0,55.0,50.0,35.0,23.0,27.0,52.0,32.0,43.0,76.0,50.0,40.0,51.0,65.0,45.0,45.0,34.0,30.0,65.0,23.0,30.0,27.0,46.0,55.0,42.0,37.0,29.0,65.0,39.0,46.0,40.0,64.0,28.0,60.0,50.0,27.0,78.0,50.0,35.0,44.0,47.0,28.0,34.0,70.0,45.0,20.0,55.0,53.0,50.0,45.0,45.0,19.0,32.0,82.0,50.0,41.0,45.0,50.0,50.0,60.0,59.0,45.0,60.0,48.0,55.0,35.0,33.0,40.0,62.0,37.0,29.0,39.0,47.0,42.0,45.0,33.0,38.0,49.0,35.0,40.0,41.0,40.0,55.0,72.0,36.0,48.0,49.0,46.0,51.0,36.0,29.0,29.0,24.0,70.0,28.0,35.0,46.0,30.0,33.0,53.0,55.0,60.0,28.0,33.0,50.0,27.0,56.0,77.0,45.0,27.0,37.0,58.0,59.0,40.0,48.0,34.0,30.0,45.0,49.0,29.0,38.0,50.0,45.0,40.0,50.0,60.0,95.0,55.0,25.0,40.0,14.0,40.0,43.0,55.0,35.0,19.0,28.0,43.0,29.0,40.0,26.0,75.0,64.0,45.0,90.0,50.0,40.0,65.0,20.0,84.0,70.0,35.0,45.0,58.0,55.0,60.0,50.0,78.0,60.0,43.0,30.0,40.0,53.0,60.0,36.0,40.0,46.0,51.0,53.0,47.0,60.0,65.0,40.0,52.0,23.0,32.0,50.0,23.0,28.0,43.0,45.0,64.0,82.0,19.0,46.0,35.0,41.0,50.0,30.0,25.0,40.0,56.0,40.0,70.0,28.0,42.0,43.0,36.0,35.0,30.0,55.0,32.0,45.0,30.0,19.0,65.0,40.0,60.0,45.0,41.0,50.0,39.0,35.0,48.0,50.0,34.0,40.0,47.0,35.0,39.0,67.0,30.0,39.0,35.0,65.0,70.0,31.0,34.0,46.0,40.0,10.0,40.0,41.0,49.0,60.0,43.0,50.0,20.0,40.0,77.0,55.0,35.0,65.0,47.0,55.0,59.0,30.0,20.0,45.0,55.0,52.0,55.0,50.0,35.0,35.0,46.0,85.0,55.0,34.0,63.0,40.0,20.0,37.0,32.0,34.0,34.0,43.0,25.0,50.0,65.0,35.0,50.0,40.0,83.0,30.0,41.0,70.0,53.0,50.0,70.0,25.0,40.0,50.0,30.0,60.0,53.0,55.0,49.0,80.0,51.0,50.0,64.0,35.0,38.0,79.0,34.0,59.0,37.0,47.0,67.0,37.0,35.0,42.0,50.0,41.0,35.0,72.0,37.0,47.0,40.0,32.0,30.0,28.0,45.0,65.0,34.0,45.0,35.0,20.0,34.0,54.0,35.0,23.0,35.0,33.0,57.0,60.0,47.0,60.0,30.0,56.0,41.0,42.0,65.0,38.0,38.0,30.0,16.0,60.0,70.0,21.0,50.0,55.0,39.0,76.0,36.0,52.0,69.0,33.0,40.0,45.0,45.0,50.0,35.0,60.0,43.0,45.0,51.0,40.0,36.0,54.0,45.0,56.0,35.0,51.0,25.0,35.0,81.0,25.0,23.0,80.0,33.0,67.0,45.0,61.0,47.0,15.0,39.0,30.0,44.0,41.0,64.0,55.0,59.0,33.0,60.0,45.0,65.0,40.0,59.0,55.0,64.0,55.0,55.0,19.0,55.0,30.0,45.0,40.0,40.0,60.0,55.0,45.0,45.0,40.0,51.0,24.0,39.0,45.0,32.0,55.0,63.0,42.0,50.0,56.0,65.0,58.0,27.0,38.0,38.0,55.0,50.0,44.0,74.0,62.0,55.0,85.0,51.0,29.0,40.0,41.0,65.0,55.0,37.0,42.0,25.0,70.0,64.0,41.0,30.0,40.0,44.0,46.0,18.0,50.0,60.0,50.0,34.0,18.0,55.0,68.0,61.0,49.0,14.0,44.0,40.0,45.0,65.0,46.0,55.0,30.0,28.0,24.0,40.0,50.0,50.0,45.0,52.0,41.0,27.0,50.0,50.0,25.0,60.0,42.0,45.0,25.0,49.0,39.0,54.0,51.0,12.0,45.0,40.0,50.0,42.0,41.0,33.0,51.0,40.0,26.0,48.0,35.0,35.0,35.0,80.0,40.0,14.0,45.0,48.0,27.0,41.0,75.0,24.0,47.0,35.0,66.0,82.0,22.0,50.0,51.0,26.0,40.0,46.0,41.0,47.0,45.0,30.0,52.0,50.0,60.0,105.0,20.0,55.0,28.0,40.0,33.0,42.0,50.0,41.0,37.0,34.0,44.0,24.0,46.0,43.0,29.0,23.0,60.0,20.0,53.0,29.0,55.0,60.0,35.0,59.0,39.0,45.0,65.0,47.0,19.0,65.0,64.0,65.0,32.0,50.0,25.0,36.0,55.0,24.0,19.0,50.0,45.0,37.0,32.0,43.0,68.0,55.0,48.0,40.0,40.0,45.0,41.0,56.0,24.0,20.0,20.0,40.0,50.0,47.0,27.0,51.0,68.0,54.0,46.0,75.0,58.0,32.0,85.0,40.0,35.0,27.0,33.0,19.0,27.0,54.0,55.0,20.0,40.0,14.0,30.0,80.0,45.0,47.0,32.0,68.0,55.0,25.0,35.0,40.0,50.0,46.0,24.0,25.0,36.0,55.0,50.0,24.0,75.0,40.0,34.0,10.0,44.0,40.0,37.0,25.0,18.0,45.0,45.0,39.0,20.0,45.0,60.0,50.0,50.0,42.0,45.0,43.0,33.0,75.0,24.0,64.0,44.0,34.0,45.0,48.0,45.0,35.0,32.0,35.0,65.0,60.0,52.0,29.0,35.0,35.0,53.0,52.0,65.0,65.0,46.0,60.0,50.0,55.0,40.0,44.0,55.0,33.0,50.0,20.0,25.0,39.0,54.0,65.0,30.0,45.0,64.0,95.0,37.0,49.0,55.0,55.0,42.0,50.0,30.0,46.0,55.0,83.0,37.0,30.0,25.0,24.0,35.0,60.0,45.0,42.0,53.0,38.0,65.0,23.0,29.0,40.0,62.0,29.0,60.0,65.0,40.0,65.0,60.0,29.0,40.0,60.0,71.0,45.0,37.0,45.0,52.0,20.0,43.0,30.0,30.0,45.0,55.0,29.0,36.0,88.0,56.0,59.0,27.0,55.0,47.0,59.0,40.0,51.0,40.0,45.0,45.0,45.0,82.0,52.0,28.0,55.0,64.0,25.0,38.0,30.0,41.0,40.0,33.0,40.0,41.0,24.0,38.0,41.0,55.0,49.0,60.0,50.0,46.0,47.0,64.0,25.0,30.0,53.0,60.0,80.0,29.0,38.0,40.0,45.0,63.0,46.0,39.0,14.0,45.0,86.0,55.0,33.0,35.0,40.0,19.0,45.0,49.0,32.0,70.0,35.0,65.0,35.0,37.0,25.0,31.0,35.0,42.0,40.0,50.0,45.0,35.0,44.0,70.0,63.0,41.0,50.0,27.0,32.0,46.0,30.0,15.0,37.0,50.0,52.0,58.0,24.0,40.0,52.0,35.0,39.0,33.0,45.0,56.0,50.0,33.0,32.0,32.0,50.0,43.0,44.0,35.0,50.0,62.0,65.0,25.0,60.0,56.0,47.0,36.0,65.0,41.0,54.0,30.0,34.0,27.0,70.0,25.0,45.0,15.0,34.0,52.0,48.0,45.0,42.0,47.0,42.0,60.0,38.0,40.0,50.0,70.0,27.0,36.0,38.0,34.0,19.0,30.0,24.0,52.0,41.0,41.0,34.0,19.0,44.0,55.0,50.0,47.0,20.0,45.0,39.0,43.0,54.0,68.0,40.0,40.0,64.0,55.0,40.0,60.0,42.0,33.0,15.0,75.0,74.0,48.0,35.0,38.0,45.0,60.0,34.0,35.0,45.0,34.0,105.0,31.0,54.0,76.0,50.0,35.0,72.0,34.0,35.0,28.0,52.0,40.0,55.0,61.0,46.0,54.0,55.0,39.0,65.0,51.0,55.0,30.0,30.0,50.0,58.0,45.0,60.0,26.0,39.0,20.0,55.0,49.0,20.0,51.0,45.0,36.0,55.0,32.0,50.0,44.0,25.0,70.0,45.0,30.0,38.0,57.0,56.0,14.0,75.0,43.0,65.0,50.0,25.0,32.0,35.0,65.0,60.0,49.0,41.0,40.0,28.0,60.0,44.0,35.0,30.0,55.0,56.0,32.0,43.0,40.0,54.0,39.0,41.0,55.0,70.0,42.0,45.0,36.0,25.0,30.0,59.0,10.0,24.0,35.0,33.0,54.0,48.0,40.0,40.0,33.0,44.0,25.0,25.0,65.0,42.0,51.0,53.0,28.0,45.0,48.0,28.0,53.0,25.0,70.0,55.0,42.0,65.0,40.0,65.0,68.0,35.0,50.0,38.0,70.0,57.0,30.0,50.0,55.0,55.0,45.0,54.0,29.0,53.0,39.0,36.0,70.0,50.0,50.0,55.0,49.0,25.0,47.0,39.0,42.0,69.0,49.0,60.0,34.0,48.0,37.0,75.0,34.0,54.0,20.0,45.0,65.0,55.0,49.0,39.0,47.0,43.0,60.0,50.0,60.0,30.0,55.0,48.0,85.0,11.0,40.0,35.0,63.0,54.0,59.0,64.0,45.0,32.0,35.0,43.0,50.0,34.0,64.0,80.0,40.0,30.0,52.0,30.0,41.0,25.0,29.0,37.0,35.0,49.0,51.0,78.0,65.0,50.0,51.0,38.0,75.0,30.0,45.0,38.0,60.0,70.0,60.0,20.0,61.0,35.0,40.0,64.0,60.0,39.0,40.0,52.0,25.0,51.0,37.0,53.0,29.0,37.0,60.0,24.0,41.0,64.0,45.0,70.0,37.0,45.0,10.0,34.0,43.0,45.0,35.0,35.0,42.0,23.0,49.0,35.0,46.0,50.0,80.0,40.0,51.0,40.0,45.0,59.0,25.0,35.0,65.0,42.0,49.0,30.0,20.0,55.0,55.0,35.0,50.0,40.0,40.0,20.0,55.0,33.0,29.0,50.0,60.0,26.0,35.0,28.0,50.0,71.0,50.0,45.0,45.0,73.0,37.0,63.0,41.0,43.0,40.0,40.0,44.0,50.0,44.0,40.0,34.0,10.0,55.0,51.0,36.0,38.0,35.0,42.0,67.0,60.0,35.0,61.0,52.0,59.0,90.0,56.0,56.0,49.0,56.0,40.0,20.0,18.0,51.0,69.0,50.0,43.0,44.0,28.0,45.0,50.0,55.0,42.0,50.0,65.0,30.0,36.0,50.0,47.0,33.0,43.0,45.0,43.0,40.0,58.0,50.0,45.0,46.0,60.0,50.0,35.0,59.0,44.0,53.0,60.0,48.0,49.0,65.0,19.0,47.0,61.0,44.0,50.0,42.0,60.0,40.0,18.0,55.0,86.0,55.0,65.0,30.0,58.0,45.0,74.0,55.0,45.0,40.0,42.0,55.0,28.0,45.0,51.0,15.0,19.0,55.0,32.0,50.0,30.0,29.0,49.0,40.0,65.0,65.0,47.0,41.0,45.0,60.0,56.0,68.0,50.0,45.0,28.0,23.0,47.0,55.0,50.0,50.0,39.0,35.0,33.0,33.0,49.0,65.0,49.0,55.0,42.0,38.0,49.0,30.0,55.0,48.0,55.0,55.0,81.0,51.0,37.0,30.0,35.0,35.0,60.0,39.0,44.0,25.0,32.0,60.0,50.0,70.0,55.0,27.0,40.0,41.0,30.0,23.0,46.0,53.0,36.0,31.0,35.0,56.0,70.0,60.0,32.0,64.0,20.0,47.0,40.0,40.0,42.0,33.0,45.0,36.0,25.0,24.0,19.0,35.0,55.0,40.0,23.0,41.0,45.0,26.0,50.0,65.0,40.0,23.0,60.0,55.0,59.0,27.0,18.0,45.0,40.0,40.0,53.0,60.0,36.0,32.0,56.0,34.0,48.0,42.0,51.0,50.0,39.0,20.0,34.0,95.0,35.0,34.0,40.0,52.0,24.0,70.0,25.0,55.0,37.0,40.0,47.0,45.0,53.0,29.0,58.0,37.0,46.0,46.0,40.0,30.0,33.0,30.0,48.0,59.0,29.0,48.0,67.0,33.0,36.0,50.0,50.0,22.0,54.0,75.0,45.0,55.0,70.0,60.0,52.0,60.0,61.0,50.0,63.0,64.0,45.0,53.0,44.0,30.0,24.0,55.0,46.0,33.0,45.0,55.0,50.0,40.0,34.0,23.0,50.0,40.0,35.0,22.0,30.0,19.0,39.0,60.0,34.0,63.0,40.0,60.0,45.0,49.0,55.0,50.0,35.0,45.0,32.0,20.0,36.0,55.0,55.0,40.0,32.0,52.0,30.0,60.0,74.0,60.0,35.0,64.0,67.0,40.0,36.0,48.0,50.0,32.0,60.0,50.0,61.0,49.0,23.0,54.0,35.0,60.0,45.0,48.0,17.0,46.0,56.0,60.0,52.0,50.0,38.0,32.0,41.0,34.0,37.0,45.0,34.0,37.0,68.0,38.0,60.0,62.0,61.0,65.0,25.0,24.0,47.0,45.0,38.0,55.0,50.0,38.0,50.0,43.0,60.0,41.0,41.0,40.0,33.0,40.0,34.0,55.0,55.0,51.0,17.0,23.0,48.0,33.0,59.0,65.0,18.0,44.0,45.0,58.0,65.0,46.0,59.0,55.0,30.0,55.0,55.0,34.0,30.0,50.0,44.0,62.0,33.0,55.0,33.0,50.0,73.0,40.0,42.0,53.0,33.0,67.0,80.0,50.0,65.0,16.0,35.0,23.0,50.0,18.0,25.0,51.0,18.0,55.0,75.0,60.0,24.0,60.0,39.0,58.0,44.0,32.0,55.0,59.0,55.0,51.0,41.0,42.0,35.0,39.0,28.0,55.0,49.0,32.0,43.0,59.0,65.0,20.0,45.0,35.0,23.0,40.0,50.0,50.0,45.0,25.0,35.0,30.0,60.0,59.0,54.0,22.0,51.0,59.0,39.0,42.0,43.0,50.0,50.0,50.0,32.0,40.0,41.0,50.0,55.0,61.0,56.0,60.0,55.0,51.0,45.0,30.0,30.0,55.0,40.0,36.0,42.0,25.0,35.0,35.0,40.0,30.0,75.0,39.0,33.0,30.0,49.0,30.0,27.0,30.0,55.0,42.0,43.0,40.0,43.0,80.0,78.0,74.0,55.0,45.0,47.0,55.0,55.0,29.0,49.0,46.0,34.0,35.0,26.0,37.0,44.0,49.0,65.0,69.0,23.0,45.0,60.0,45.0,43.0,60.0,74.0,14.0,41.0,41.0,44.0,50.0,59.0,30.0,21.0,39.0,55.0,45.0,36.0,40.0,55.0,43.0,46.0,60.0,34.0,20.0,48.0,33.0,65.0,40.0,34.0,47.0,70.0,45.0,45.0,67.0,35.0,70.0,53.0,15.0,47.0,37.0,27.0,28.0,41.0,49.0,24.0,50.0,26.0,35.0,53.0,53.0,35.0,47.0,27.0,24.0,46.0,45.0,30.0,39.0,55.0,42.0,27.0,60.0,47.0,31.0,46.0,52.0,70.0,29.0,58.0,60.0,51.0,41.0,44.0,50.0,51.0,50.0,40.0,29.0,44.0,63.0,103.0,40.0,37.0,62.0,30.0,60.0,67.0,50.0,44.0,30.0,63.0,32.0,51.0,30.0,45.0,45.0,55.0,50.0,55.0,65.0,20.0,43.0,43.0,15.0,55.0,53.0,42.0,56.0,45.0,30.0,36.0,50.0,27.0,40.0,40.0,45.0,15.0,34.0,39.0,64.0,33.0,45.0,32.0,35.0,50.0,40.0,53.0,47.0,90.0,35.0,49.0,40.0,51.0,49.0,44.0,50.0,35.0,42.0,26.0,45.0,34.0,25.0,65.0,45.0,43.0,30.0,72.0,46.0,30.0,28.0,33.0,35.0,29.0,55.0,49.0,55.0,50.0,60.0,73.0,41.0,65.0,53.0,38.0,42.0,50.0,51.0,51.0,30.0,45.0,40.0,67.0,75.0,65.0,43.0,41.0,55.0,40.0,31.0,52.0,65.0,50.0,63.0,71.0,55.0,41.0,44.0,41.0,58.0,24.0,40.0,10.0,35.0,45.0,56.0,65.0,60.0,37.0,51.0,46.0,35.0,70.0,20.0,62.0,64.0,42.0,32.0,55.0,45.0,55.0,35.0,80.0,74.0,45.0,60.0,35.0,42.0,29.0,50.0,35.0,43.0,85.0,58.0,27.0,45.0,35.0,50.0,30.0,39.0,36.0,50.0,40.0,65.0,32.0,33.0,64.0,36.0,55.0,69.0,32.0,54.0,62.0,23.0,40.0,80.0,60.0,35.0,55.0,47.0,55.0,45.0,63.0,23.0,54.0,43.0,20.0,37.0,50.0,50.0,50.0,51.0,55.0,33.0,51.0,52.0,27.0,60.0,11.0,35.0,86.0,70.0,42.0,54.0,48.0,45.0,50.0,65.0,30.0,32.0,35.0,44.0,44.0,43.0,47.0,72.0,52.0,60.0,35.0,60.0,38.0,37.0,50.0,43.0,44.0,55.0,54.0,43.0,41.0,40.0,42.0,50.0,53.0,61.0,18.0,32.0,55.0,24.0,30.0,38.0,32.0,50.0,33.0,44.0,47.0,49.0,49.0,45.0,47.0,43.0,55.0,70.0,43.0,40.0,43.0,47.0,35.0,50.0,45.0,60.0,29.0,50.0,41.0,20.0,59.0,30.0,42.0,16.0,70.0,30.0,35.0,25.0,46.0,41.0,35.0,46.0,50.0,50.0,41.0,45.0,50.0,56.0,44.0,96.0,44.0,37.0,56.0,20.0,28.0,46.0,67.0,44.0,36.0,65.0,50.0,15.0,25.0,23.0,40.0,44.0,52.0,45.0,55.0,35.0,60.0,27.0,46.0,30.0,50.0,45.0,59.0,47.0,40.0,51.0,66.0,41.0,33.0,44.0,25.0,11.0,60.0,54.0,35.0,46.0,69.0,33.0,48.0,55.0,54.0,32.0,29.0,45.0,60.0,35.0,21.0,40.0,52.0,35.0,51.0,65.0,55.0,24.0,36.0,58.0,45.0,47.0,25.0,61.0,43.0,38.0,33.0,48.0,40.0,32.0,45.0,45.0,46.0,40.0,20.0,59.0,18.0,30.0,30.0,53.0,13.0,35.0,24.0,46.0,30.0,50.0,32.0,75.0,50.0,35.0,40.0,60.0,38.0,11.0,65.0,35.0,25.0,42.0,48.0,51.0,65.0,42.0,50.0,14.0,48.0,45.0,50.0,50.0,70.0,55.0,32.0,36.0,34.0,48.0,30.0,29.0,30.0,40.0,30.0,34.0,75.0,25.0,20.0,36.0,34.0,55.0,51.0,41.0,58.0,82.0,41.0,60.0,80.0,43.0,41.0,60.0,29.0,55.0,39.0,50.0,60.0,47.0,29.0,65.0,40.0,59.0,31.0,37.0,52.0,24.0,28.0,48.0,29.0,14.0,32.0,40.0,36.0,20.0,41.0,35.0,55.0,30.0,46.0,18.0,25.0,45.0,28.0,46.0,60.0,42.0,53.0,74.0,35.0,45.0,35.0,45.0,58.0,55.0,74.0,42.0,50.0,48.0,32.0,47.0,12.0,50.0,45.0,65.0,45.0,38.0,40.0,65.0,34.0,56.0,55.0,65.0,32.0,55.0,65.0,70.0,36.0,55.0,50.0,27.0,50.0,36.0,49.0,33.0,60.0,50.0,97.0,65.0,30.0,60.0,33.0,55.0,50.0,50.0,34.0,56.0,65.0,55.0,37.0,59.0,25.0,55.0,50.0,54.0,35.0,43.0,26.0,25.0,68.0,45.0,45.0,59.0,39.0,45.0,57.0,35.0,35.0,45.0,36.0,44.0,50.0,55.0,45.0,40.0,40.0,36.0,65.0,46.0,60.0,65.0,59.0,60.0,35.0,29.0,70.0,28.0,62.0,45.0,70.0,57.0,39.0,18.0,24.0,60.0,26.0,55.0,65.0,50.0,85.0,29.0,44.0,78.0,56.0,38.0,38.0,65.0,35.0,41.0,55.0,51.0,100.0,40.0,70.0,51.0,23.0,23.0,23.0,40.0,49.0,39.0,54.0,48.0,80.0,55.0,45.0,40.0,45.0,55.0,20.0,66.0,25.0,37.0,24.0,50.0,55.0,41.0,60.0,27.0,36.0,65.0,40.0,59.0,55.0,42.0,39.0,36.0,40.0,50.0,35.0,62.0,48.0,39.0,32.0,29.0,32.0,35.0,49.0,45.0,50.0,33.0,55.0,55.0,46.0,65.0,53.0,35.0,33.0,50.0,30.0,40.0,45.0,60.0,50.0,42.0,43.0,50.0,65.0,33.0,49.0,35.0,36.0,89.0,47.0,42.0,50.0,38.0,46.0,46.0,43.0,53.0,33.0,20.0,48.0,24.0,52.0,32.0,45.0,55.0,42.0,82.0,36.0,16.0,47.0,65.0,70.0,60.0,15.0,43.0,47.0,34.0,21.0,50.0,36.0,33.0,29.0,51.0,65.0,19.0,73.0,35.0,45.0,44.0,40.0,55.0,105.0,55.0,59.0,41.0,25.0,64.0,43.0,44.0,37.0,47.0,40.0,61.0,32.0,65.0,50.0,55.0,25.0,38.0,24.0,50.0,58.0,41.0,50.0,21.0,45.0,60.0,45.0,50.0,60.0,42.0,49.0,55.0,89.0,43.0,101.0,39.0,34.0,45.0,65.0,41.0,52.0,25.0,50.0,45.0,68.0,50.0,58.0,19.0,33.0,60.0,75.0,55.0,65.0,64.0,29.0,56.0,50.0,18.0,20.0,35.0,46.0,63.0,35.0,44.0,61.0,65.0,31.0,28.0,24.0,39.0,90.0,39.0,32.0,60.0,48.0,23.0,79.0,60.0,85.0,59.0,35.0,15.0,64.0,41.0,60.0,41.0,50.0,39.0,46.0,64.0,59.0,50.0,18.0,53.0,58.0,55.0,42.0,30.0,50.0,90.0,45.0,19.0,40.0,24.0,42.0,28.0,45.0,55.0,25.0,46.0,40.0,50.0,45.0,45.0,50.0,69.0,50.0,37.0,29.0,47.0,50.0,46.0,59.0,36.0,27.0,50.0,42.0,41.0,62.0,35.0,47.0,47.0,55.0,40.0,66.0,14.0,70.0,50.0,42.0,44.0,15.0,71.0,60.0,32.0,39.0,38.0,30.0,59.0,40.0,55.0,25.0,19.0,25.0,55.0,43.0,45.0,47.0,59.0,55.0,43.0,60.0,47.0,45.0,61.0,25.0,55.0,43.0,55.0,65.0,45.0,47.0,26.0,35.0,55.0,30.0,65.0,73.0,36.0,50.0,14.0,28.0,25.0,33.0,28.0,65.0,43.0,50.0,55.0,54.0,19.0,33.0,40.0,35.0,60.0,45.0,55.0,45.0,14.0,45.0,44.0,38.0,55.0,35.0,49.0,52.0,14.0,33.0,35.0,39.0,51.0,24.0,40.0,43.0,29.0,35.0,30.0,64.0,41.0,60.0,33.0,52.0,45.0,40.0,51.0,65.0,23.0,44.0,47.0,75.0,62.0,38.0,19.0,41.0,90.0,55.0,70.0,42.0,46.0,60.0,45.0,34.0,14.0,30.0,50.0,62.0,55.0,65.0,42.0,95.0,41.0,35.0,50.0,54.0,60.0,29.0,60.0,51.0,50.0,20.0,65.0,64.0,55.0,28.0,39.0,43.0,43.0,85.0,60.0,36.0,36.0,42.0,53.0,44.0,36.0,85.0,46.0,32.0,85.0,60.0,19.0,30.0,41.0,46.0,18.0,37.0,29.0,59.0,54.0,51.0,68.0,45.0,41.0,31.0,37.0,42.0,40.0,37.0,35.0,45.0,51.0,45.0,23.0,86.0,35.0,41.0,55.0,45.0,26.0,51.0,34.0,37.0,35.0,35.0,43.0,70.0,28.0,54.0,50.0,50.0,43.0,40.0,19.0,50.0,40.0,57.0,49.0,48.0,15.0,55.0,40.0,67.0,33.0,31.0,65.0,20.0,66.0,30.0,38.0,65.0,38.0,43.0,35.0,47.0,41.0,55.0,49.0,27.0,60.0,46.0,15.0,55.0,15.0,50.0,47.0,39.0,43.0,47.0,45.0,45.0,50.0,40.0,52.0,50.0,42.0,32.0,14.0,43.0,28.0,20.0,75.0,30.0,38.0,51.0,49.0,45.0,13.0,60.0,60.0,25.0,51.0,32.0,37.0,29.0,80.0,47.0,20.0,45.0,45.0,60.0,35.0,40.0,48.0,45.0,42.0,43.0,48.0,55.0,38.0,34.0,30.0,30.0,53.0,75.0,65.0,49.0,62.0,50.0,23.0,53.0,60.0,40.0,47.0,25.0,32.0,49.0,60.0,29.0,50.0,57.0,60.0,20.0,32.0,47.0,38.0,49.0,47.0,37.0,51.0,52.0,50.0,60.0,55.0,60.0,56.0,50.0,50.0,50.0,41.0,20.0,65.0,40.0,37.0,35.0,65.0,45.0,36.0,51.0,39.0,82.0,38.0,32.0,60.0,49.0,48.0,46.0,49.0,58.0,41.0,38.0,39.0,65.0,45.0,50.0,72.0,49.0,62.0,17.0,70.0,50.0,70.0,45.0,47.0,40.0,55.0,48.0,50.0,43.0,45.0,50.0,41.0,55.0,41.0,65.0,54.0,45.0,50.0,36.0,9.0,28.0,40.0,60.0,40.0,55.0,50.0,32.0,54.0,60.0,25.0,40.0,20.0,45.0,44.0,45.0,63.0,57.0,60.0,34.0,43.0,36.0,50.0,30.0,40.0,50.0,50.0,55.0,70.0,54.0,73.0,35.0,75.0,60.0,23.0,44.0,48.0,34.0,35.0,80.0,42.0,19.0,40.0,44.0,51.0,55.0,36.0,55.0,56.0,48.0,58.0,38.0,24.0,55.0,11.0,45.0,50.0,30.0,29.0,41.0,55.0,60.0,33.0,46.0,38.0,43.0,43.0,33.0,40.0,46.0,33.0,49.0,25.0,25.0,43.0,55.0,31.0,80.0,40.0,41.0,50.0,60.0,23.0,45.0,60.0,45.0,27.0,43.0,39.0,15.0,20.0,52.0,65.0,70.0,28.0,60.0,50.0,50.0,50.0,55.0,85.0,32.0,39.0,23.0,38.0,52.0,41.0,65.0,15.0,60.0,28.0,45.0,20.0,14.0,25.0,44.0,47.0,30.0,44.0,65.0,36.0,45.0,39.0,53.0,40.0,41.0,50.0,52.0,68.0,65.0,42.0,36.0,15.0,80.0,21.0,33.0,47.0,64.0,82.0,35.0,45.0,30.0,60.0,26.0,58.0,42.0,35.0,50.0,52.0,80.0,40.0,35.0,35.0,69.0,19.0,30.0,50.0,39.0,42.0,48.0,50.0,65.0,45.0,45.0,45.0,72.0,32.0,32.0,20.0,54.0,40.0,55.0,40.0,55.0,24.0,37.0,55.0,51.0,55.0,21.0,59.0,61.0,36.0,59.0,70.0,64.0,64.0,39.0,38.0,65.0,47.0,31.0,54.0,65.0,40.0,32.0,54.0,40.0,20.0,52.0,28.0,20.0,37.0,15.0,44.0,52.0,34.0,30.0,50.0,30.0,50.0,34.0,68.0,45.0,50.0,52.0,47.0,40.0,40.0,45.0,75.0,80.0,50.0,60.0,80.0,46.0,55.0,60.0,27.0,54.0,39.0,58.0,51.0,35.0,45.0,38.0,22.0,62.0,45.0,37.0,38.0,35.0,65.0,13.0,27.0,34.0,50.0,48.0,50.0,60.0,55.0,58.0,29.0,50.0,38.0,40.0,41.0,39.0,45.0,35.0,20.0,23.0,50.0,24.0,85.0,47.0,48.0,60.0,50.0,19.0,32.0,47.0,32.0,60.0,27.0,32.0,18.0,60.0,50.0,41.0,55.0,49.0,58.0,19.0,85.0,31.0,19.0,39.0,80.0,55.0,54.0,12.0,41.0,40.0,33.0,50.0,40.0,40.0,65.0,24.0,40.0,32.0,49.0,46.0,54.0,49.0,45.0,48.0,51.0,35.0,35.0,40.0,46.0,22.0,89.0,37.0,58.0,72.0,36.0,50.0,47.0,35.0,47.0,20.0,45.0,55.0,31.0,50.0,56.0,60.0,34.0,58.0,41.0,55.0,46.0,51.0,60.0,25.0,62.0,33.0,45.0,55.0,59.0,37.0,23.0,11.0,49.0,43.0,60.0,73.0,41.0,55.0,19.0,45.0,64.0,55.0,45.0,50.0,47.0,60.0,60.0,65.0,35.0,37.0,50.0,40.0,50.0,61.0,35.0,43.0,30.0,54.0,85.0,20.0,58.0,50.0,60.0,30.0,75.0,40.0,32.0,29.0,39.0,58.0,20.0,55.0,54.0,55.0,64.0,20.0,52.0,37.0,64.0,47.0,55.0,81.0,49.0,44.0,50.0,35.0,50.0,38.0,30.0,49.0,47.0,60.0,55.0,25.0,80.0,45.0,43.0,49.0,45.0,62.0,32.0,36.0,45.0,55.0,66.0,47.0,32.0,48.0,44.0,42.0,27.0,41.0,29.0,27.0,44.0,55.0,44.0,40.0,45.0,33.0,37.0,52.0,50.0,36.0,35.0,35.0,41.0,58.0,51.0,35.0,23.0,70.0,60.0,44.0,29.0,59.0,41.0,37.0,45.0,45.0,37.0,56.0,41.0,47.0,65.0,38.0,25.0,46.0,38.0,70.0,37.0,65.0,52.0,47.0,50.0,55.0,30.0,53.0,44.0,32.0,27.0,33.0,25.0,54.0,67.0,33.0,30.0,54.0,40.0,60.0,35.0,41.0,38.0,51.0,46.0,32.0,34.0,49.0,24.0,40.0,55.0,50.0,53.0,19.0,59.0,35.0,24.0,55.0,56.0,40.0,35.0,50.0,50.0,76.0,35.0,45.0,45.0,45.0,55.0,44.0,45.0,30.0,23.0,64.0,59.0,23.0,50.0,64.0,47.0,55.0,25.0,47.0,70.0,38.0,50.0,55.0,43.0,45.0,36.0,55.0,53.0,35.0,35.0,25.0,58.0,40.0,65.0,52.0,50.0,65.0,37.0,33.0,35.0,46.0,35.0,32.0,43.0,43.0,25.0,55.0,47.0,65.0,51.0,35.0,29.0,75.0,47.0,65.0,55.0,55.0,36.0,55.0,40.0,25.0,35.0,43.0,40.0,45.0,60.0,52.0,37.0,40.0,35.0,44.0,55.0,23.0,30.0,20.0,49.0,65.0,34.0,47.0,35.0,64.0,46.0,40.0,72.0,63.0,38.0,55.0,47.0,28.0,38.0,59.0,65.0,35.0,41.0,20.0,38.0,60.0,39.0,55.0,40.0,70.0,70.0,60.0,45.0,60.0,40.0,37.0,13.0,80.0,43.0,74.0,59.0,44.0,71.0,23.0,55.0,54.0,45.0,30.0,57.0,39.0,65.0,26.0,41.0,29.0,34.0,40.0,54.0,51.0,32.0,58.0,25.0,33.0,60.0,60.0,38.0,43.0,24.0,40.0,50.0,28.0,29.0,14.0,51.0,36.0,50.0,22.0,25.0,66.0,85.0,47.0,40.0,53.0,37.0,59.0,41.0,32.0,24.0,65.0,55.0,60.0,48.0,50.0,60.0,25.0,20.0,39.0,96.0,50.0,22.0,47.0,58.0,45.0,48.0,38.0,35.0,43.0,53.0,50.0,44.0,40.0,43.0,60.0,60.0,50.0,60.0,50.0,35.0,47.0,65.0,95.0,29.0,32.0,26.0,25.0,53.0,40.0,36.0,52.0,47.0,41.0,50.0,40.0,55.0,32.0,38.0,45.0,53.0,70.0,33.0,65.0,46.0,55.0,38.0,50.0,19.0,65.0,47.0,60.0,39.0,35.0,55.0,35.0,54.0,25.0,70.0,37.0,49.0,32.0,36.0,26.0,34.0,75.0,59.0,36.0,65.0,55.0,55.0,75.0,59.0,43.0,40.0,54.0,50.0,64.0,10.0,40.0,36.0,41.0,65.0,49.0,30.0,48.0,85.0,55.0,70.0,49.0,55.0,40.0,23.0,45.0,55.0,45.0,42.0,60.0,37.0,33.0,52.0,41.0,37.0,55.0,50.0,38.0,70.0,70.0,70.0,55.0,47.0,65.0,86.0,58.0,40.0,70.0,55.0,39.0,65.0,39.0,25.0,23.0,55.0,55.0,40.0,45.0,90.0,35.0,50.0,59.0,56.0,50.0,30.0,37.0,50.0,45.0,73.0,24.0,59.0,54.0,60.0,45.0,35.0,31.0,60.0,47.0,54.0,80.0,55.0,60.0,19.0,45.0,50.0,71.0,50.0,30.0,60.0,50.0,45.0,24.0,61.0,50.0,52.0,31.0,75.0,46.0,37.0,49.0,59.0,45.0,59.0,40.0,30.0,55.0,45.0,18.0,37.0,46.0,55.0,50.0,29.0,45.0,55.0,45.0,59.0,65.0,46.0,83.0,46.0,50.0,79.0,55.0,30.0,60.0,90.0,70.0,29.0,65.0,80.0,90.0,38.0,60.0,25.0,56.0,49.0,25.0,40.0,37.0,41.0,19.0,20.0,45.0,62.0,55.0,33.0,50.0,84.0,47.0,28.0,44.0,30.0,46.0,55.0,57.0,55.0,58.0,55.0,72.0,49.0,45.0,38.0,70.0,45.0,40.0,29.0,65.0,45.0,51.0,36.0,50.0,35.0,44.0,60.0,41.0,55.0,43.0,48.0,75.0,56.0,36.0,46.0,69.0,95.0,51.0,50.0,45.0,45.0,46.0,49.0,44.0,23.0,26.0,47.0,65.0,32.0,47.0,29.0,32.0,30.0,42.0,27.0,40.0,55.0,24.0,62.0,52.0,44.0,55.0,45.0,73.0,62.0,43.0,42.0,67.0,34.0,51.0,25.0,45.0,24.0,61.0,50.0,60.0,23.0,41.0,47.0,23.0,64.0,50.0,46.0,45.0,14.0,40.0,60.0,43.0,59.0,52.0,37.0,55.0,45.0,58.0,44.0,59.0,20.0,46.0,68.0,35.0,35.0,51.0,9.0,37.0,39.0,43.0,65.0,60.0,31.0,48.0,36.0,38.0,90.0,32.0,35.0,42.0,40.0,70.0,73.0,24.0,45.0,52.0,20.0,35.0,37.0,35.0,39.0,58.0,46.0,55.0,14.0,70.0,55.0,64.0,46.0,55.0,49.0,60.0,40.0,96.0,52.0,33.0,30.0,87.0,43.0,55.0,49.0,36.0,50.0,45.0,40.0,67.0,60.0,25.0,9.0,60.0,20.0,55.0,55.0,40.0,40.0,43.0,60.0,36.0,65.0,57.0,75.0,41.0,32.0,65.0,40.0,37.0,19.0,55.0,30.0,40.0,66.0,65.0,70.0,35.0,45.0,54.0,38.0,48.0,25.0,35.0,24.0,31.0,23.0,40.0,45.0,51.0,45.0,20.0,55.0,35.0,20.0,29.0,45.0,32.0,50.0,35.0,45.0,60.0,61.0,55.0,85.0,30.0,41.0,59.0,54.0,36.0,28.0,63.0,28.0,42.0,53.0,50.0,31.0,60.0,59.0,53.0,27.0,58.0,38.0,46.0,40.0,32.0,40.0,40.0,39.0,60.0,45.0,27.0,38.0,29.0,41.0,33.0,50.0,41.0,67.0,55.0,70.0,25.0,40.0,50.0,35.0,48.0,40.0,55.0,27.0,30.0,33.0,41.0,50.0,37.0,40.0,27.0,39.0,40.0,18.0,55.0,46.0,47.0,64.0,23.0,41.0,29.0,40.0,46.0,40.0,50.0,47.0,28.0,15.0,50.0,34.0,45.0,71.0,55.0,60.0,55.0,74.0,37.0,55.0,60.0,45.0,41.0,63.0,50.0,28.0,42.0,41.0,27.0,50.0,48.0,38.0,47.0,45.0,38.0,65.0,53.0,65.0,40.0,41.0,24.0,50.0,50.0,15.0,49.0,61.0,40.0,36.0,49.0,55.0,47.0,58.0,44.0,55.0,19.0,39.0,55.0,56.0,35.0,50.0,71.0,55.0,80.0,50.0,40.0,25.0,60.0,60.0,31.0,50.0,65.0,44.0,22.0,53.0,48.0,45.0,80.0,45.0,60.0,46.0,50.0,65.0,44.0,38.0,53.0,80.0,50.0,20.0,43.0,30.0,80.0,40.0,49.0,53.0,42.0,38.0,7.0,20.0,40.0,50.0,32.0,47.0,40.0,20.0,65.0,34.0,58.0,30.0,55.0,44.0,42.0,25.0,42.0,32.0,52.0,32.0,60.0,36.0,50.0,19.0,33.0,52.0,50.0,90.0,50.0,65.0,20.0,50.0,70.0,60.0,67.0,14.0,30.0,50.0,37.0,55.0,55.0,28.0,25.0,70.0,39.0,55.0,19.0,37.0,55.0,35.0,61.0,53.0,50.0,50.0,60.0,63.0,45.0,49.0,40.0,48.0,60.0,40.0,65.0,50.0,34.0,24.0,52.0,40.0,50.0,40.0,78.0,29.0,53.0,47.0,32.0,40.0,45.0,64.0,56.0,31.0,59.0,36.0,37.0,34.0,41.0,55.0,25.0,50.0,55.0,58.0,50.0,36.0,35.0,32.0,50.0,34.0,80.0,60.0,40.0,55.0,55.0,55.0,37.0,62.0,43.0,45.0,25.0,83.0,23.0,45.0,45.0,46.0,60.0,54.0,28.0,55.0,38.0,45.0,49.0,50.0,80.0,35.0,65.0,15.0,65.0,55.0,45.0,19.0,40.0,60.0,38.0,72.0,29.0,37.0,58.0,65.0,45.0,37.0,55.0,74.0,28.0,60.0,70.0,50.0,38.0,40.0,43.0,40.0,29.0,60.0,54.0,110.0,44.0,17.0,39.0,20.0,35.0,51.0,34.0,50.0,45.0,40.0,30.0,39.0,45.0,52.0,50.0,39.0,75.0,70.0,65.0,19.0,50.0,60.0,40.0,45.0,45.0,45.0,42.0,30.0,55.0,40.0,45.0,37.0,37.0,29.0,54.0,15.0,55.0,46.0,55.0,33.0,40.0,30.0,35.0,55.0,75.0,33.0,50.0,42.0,50.0,55.0,40.0,25.0,35.0,60.0,34.0,59.0,56.0,30.0,27.0,73.0,64.0,45.0,45.0,49.0,34.0,49.0,45.0,65.0,36.0,47.0,34.0,49.0,43.0,34.0,29.0,70.0,62.0,55.0,50.0,32.0,50.0,40.0,30.0,49.0,74.0,52.0,16.0,39.0,44.0,41.0,74.0,40.0,44.0,55.0,42.0,35.0,50.0,30.0,44.0,37.0,51.0,45.0,53.0,29.0,35.0,50.0,42.0,37.0,50.0,50.0,60.0,38.0,35.0,24.0,37.0,54.0,55.0,55.0,64.0,52.0,24.0,32.0,45.0,44.0,25.0,50.0,30.0,60.0,63.0,43.0,85.0,77.0,29.0,25.0,65.0,27.0,54.0,28.0,45.0,29.0,32.0,64.0,54.0,50.0,29.0,60.0,50.0,55.0,40.0,23.0,46.0,61.0,56.0,70.0,50.0,45.0,38.0,35.0,49.0,24.0,27.0,64.0,33.0,70.0,35.0,48.0,39.0,29.0,36.0,60.0,50.0,59.0,41.0,51.0,54.0,51.0,18.0,36.0,18.0,80.0,39.0,50.0,60.0,24.0,23.0,14.0,40.0,60.0,43.0,55.0,95.0,45.0,34.0,27.0,38.0,36.0,60.0,64.0,82.0,54.0,25.0,55.0,21.0,45.0,72.0,65.0,20.0,17.0,69.0,60.0,45.0,42.0,40.0,48.0,39.0,34.0,60.0,42.0,65.0,10.0,41.0,61.0,65.0,44.0,20.0,35.0,44.0,32.0,38.0,34.0,47.0,41.0,44.0,52.0,55.0,48.0,23.0,50.0,56.0,25.0,40.0,35.0,54.0,53.0,35.0,60.0,38.0,50.0,40.0,40.0,55.0,45.0,67.0,34.0,85.0,43.0,48.0,18.0,65.0,28.0,40.0,36.0,48.0,54.0,45.0,56.0,37.0,39.0,38.0,92.0,43.0,44.0,47.0,65.0,55.0,41.0,37.0,40.0,60.0,30.0,40.0,67.0,70.0,50.0,48.0,54.0,35.0,65.0,29.0,27.0,50.0,50.0,50.0,43.0,34.0,65.0,50.0,33.0,44.0,56.0,55.0,31.0,47.0,33.0,65.0,35.0,50.0,34.0,40.0,37.0,27.0,15.0,14.0,50.0,40.0,40.0,60.0,27.0,75.0,47.0,60.0,55.0,25.0,60.0,52.0,39.0,58.0,39.0,60.0,78.0,35.0,20.0,40.0,45.0,48.0,50.0,28.0,50.0,50.0,32.0,55.0,15.0,37.0,70.0,42.0,70.0,48.0,60.0,23.0,70.0,63.0,36.0,24.0,50.0,45.0,32.0,55.0,33.0,38.0,32.0,36.0,65.0,36.0,55.0,45.0,40.0,51.0,53.0,65.0,41.0,50.0,32.0,54.0,39.0,65.0,50.0,44.0,39.0,32.0,29.0,32.0,45.0,55.0,35.0,45.0,53.0,75.0,24.0,14.0,45.0,67.0,40.0,60.0,16.0,100.0,48.0,29.0,50.0,50.0,40.0,60.0,26.0,29.0,62.0,39.0,60.0,41.0,32.0,62.0,20.0,38.0,32.0,55.0,54.0,20.0,51.0,50.0,45.0,27.0,60.0,40.0,45.0,40.0,25.0,51.0,71.0,29.0,47.0,27.0,42.0,44.0,59.0,103.0,40.0,75.0,63.0,48.0,30.0,77.0,65.0,75.0,34.0,65.0,45.0,25.0,65.0,60.0,25.0,47.0,45.0,48.0,53.0,75.0,40.0,35.0,58.0,18.0,54.0,70.0,34.0,41.0,52.0,59.0,17.0,40.0,64.0,53.0,50.0,60.0,61.0,75.0,41.0,29.0,30.0,37.0,48.0,54.0,50.0,29.0,29.0,41.0,27.0,53.0,34.0,59.0,17.0,48.0,55.0,50.0,56.0,24.0,30.0,43.0,30.0,25.0,72.0,35.0,19.0,34.0,45.0,49.0,65.0,32.0,43.0,18.0,60.0,50.0,60.0,46.0,42.0,61.0,24.0,55.0,55.0,58.0,30.0,45.0,60.0,25.0,49.0,30.0,35.0,55.0,19.0,55.0,49.0,50.0,30.0,14.0,27.0,43.0,61.0,47.0,39.0,25.0,75.0,38.0,47.0,37.0,37.0,43.0,70.0,47.0,44.0,50.0,40.0,50.0,55.0,20.0,45.0,46.0,37.0,45.0,58.0,65.0,30.0,55.0,21.0,60.0,75.0,44.0,55.0,14.0,61.0,80.0,45.0,51.0,30.0,65.0,28.0,60.0,38.0,60.0,60.0,37.0,45.0,41.0,44.0,65.0,41.0,40.0,60.0,40.0,47.0,55.0,85.0,39.0,59.0,32.0,55.0,25.0,12.0,55.0,37.0,82.0,75.0,59.0,40.0,39.0,40.0,37.0,30.0,19.0,41.0,50.0,38.0,65.0,40.0,34.0,13.0,41.0,33.0,55.0,39.0,33.0,27.0,36.0,42.0,43.0,46.0,95.0,21.0,55.0,60.0,46.0,57.0,52.0,74.0,33.0,35.0,38.0,33.0,49.0,70.0,50.0,35.0,40.0,30.0,60.0,15.0,40.0,45.0,45.0,55.0,36.0,41.0,60.0,60.0,74.0,40.0,27.0,42.0,92.0,49.0,35.0,51.0,27.0,30.0,48.0,54.0,70.0,30.0,29.0,43.0,19.0,37.0,50.0,24.0,75.0,48.0,36.0,40.0,50.0,28.0,23.0,42.0,50.0,64.0,23.0,29.0,65.0,51.0,41.0,15.0,65.0,39.0,50.0,40.0,50.0,40.0,42.0,38.0,40.0,60.0,51.0,29.0,38.0,47.0,50.0,65.0,86.0,30.0,36.0,35.0,44.0,47.0,50.0,69.0,32.0,59.0,51.0,43.0,40.0,34.0,45.0,44.0,50.0,39.0,25.0,40.0,64.0,59.0,28.0,35.0,54.0,35.0,20.0,50.0,59.0,66.0,14.0,53.0,36.0,44.0,20.0,66.0,40.0,62.0,98.0,40.0,55.0,50.0,40.0,59.0,65.0,44.0,42.0,33.0,27.0,40.0,18.0,28.0,60.0,64.0,45.0,40.0,33.0,50.0,34.0,50.0,45.0,55.0,55.0,39.0,59.0,45.0,20.0,52.0,60.0,86.0,53.0,75.0,60.0,30.0,40.0,55.0,30.0,58.0,67.0,47.0,38.0,40.0,60.0,80.0,38.0,50.0,50.0,41.0,40.0,46.0,40.0,45.0,40.0,45.0,55.0,59.0,30.0,20.0,55.0,65.0,39.0,47.0,59.0,50.0,28.0,50.0,56.0,47.0,25.0,65.0,35.0,32.0,29.0,29.0,75.0,39.0,50.0,62.0,64.0,38.0,38.0,38.0,45.0,65.0,49.0,45.0,23.0,65.0,33.0,29.0,43.0,73.0,51.0,10.0,29.0,50.0,45.0,23.0,25.0,46.0,40.0,34.0,41.0,71.0,40.0,70.0,44.0,17.0,85.0,37.0,32.0,59.0,25.0,45.0,62.0,45.0,62.0,45.0,43.0,35.0,35.0,31.0,36.0,39.0,50.0,48.0,55.0,60.0,55.0,55.0,39.0,40.0,60.0,37.0,40.0,70.0,20.0,38.0,54.0,53.0,37.0,50.0,35.0,50.0,50.0,52.0,86.0,70.0,48.0,40.0,65.0,51.0,50.0,37.0,40.0,45.0,56.0,38.0,71.0,45.0,11.0,65.0,80.0,38.0,39.0,30.0,53.0,39.0,60.0,50.0,38.0,59.0,41.0,50.0,49.0,60.0,38.0,64.0,15.0,39.0,32.0,41.0,48.0,60.0,59.0,44.0,24.0,70.0,44.0,35.0,58.0,60.0,90.0,60.0,42.0,62.0,19.0,20.0,56.0,48.0,40.0,42.0,85.0,33.0,43.0,26.0,60.0,60.0,23.0,48.0,23.0,27.0,45.0,32.0,35.0,36.0,37.0,35.0,37.0,42.0,32.0,24.0,50.0,53.0,31.0,75.0,54.0,46.0,37.0,45.0,65.0,46.0,30.0,30.0,86.0,37.0,47.0,38.0,97.0,65.0,43.0,45.0,60.0,32.0,49.0,44.0,53.0,80.0,45.0,34.0,45.0,33.0,32.0,40.0,46.0,30.0,32.0,44.0,34.0,29.0,43.0,55.0,56.0,72.0,68.0,100.0,36.0,70.0,20.0,40.0,32.0,46.0,55.0,19.0,45.0,50.0,60.0,45.0,42.0,23.0,33.0,50.0,37.0,75.0,75.0,44.0,65.0,60.0,40.0,60.0,28.0,34.0,32.0,49.0,55.0,50.0,42.0,55.0,36.0,55.0,40.0,44.0,55.0,30.0,55.0,69.0,55.0,65.0,87.0,35.0,45.0,35.0,65.0,55.0,42.0,61.0,65.0,49.0,38.0,50.0,19.0,40.0,35.0,46.0,37.0,60.0,45.0,55.0,73.0,60.0,40.0,41.0,41.0,45.0,25.0,35.0,45.0,35.0,29.0,38.0,44.0,69.0,48.0,41.0,40.0,50.0,35.0,35.0,55.0,49.0,58.0,35.0,45.0,53.0,52.0,51.0,45.0,50.0,50.0,35.0,57.0,65.0,23.0,41.0,50.0,44.0,41.0,50.0,56.0,60.0,27.0,80.0,60.0,47.0,39.0,40.0,60.0,51.0,32.0,63.0,110.0,45.0,34.0,60.0,28.0,34.0,67.0,47.0,40.0,34.0,38.0,39.0,28.0,44.0,56.0,24.0,45.0,25.0,44.0,54.0,50.0,78.0,50.0,55.0,40.0,30.0,40.0,55.0,80.0,41.0,35.0,46.0,45.0,50.0,80.0,36.0,53.0,50.0,24.0,56.0,32.0,38.0,57.0,25.0,15.0,10.0,43.0,45.0,50.0,48.0,56.0,65.0,56.0,35.0,50.0,50.0,40.0,29.0,60.0,15.0,55.0,29.0,60.0,50.0,34.0,49.0,36.0,25.0,60.0,41.0,50.0,41.0,35.0,50.0,42.0,60.0,40.0,51.0,7.0,67.0,42.0,30.0,64.0,25.0,45.0,28.0,55.0,50.0,60.0,55.0,25.0,55.0,60.0,42.0,33.0,30.0,29.0,32.0,60.0,65.0,29.0,35.0,40.0,50.0,50.0,19.0,54.0,19.0,52.0,53.0,33.0,60.0,45.0,9.0,60.0,45.0,60.0,71.0,23.0,85.0,36.0,46.0,70.0,50.0,65.0,50.0,55.0,46.0,13.0,34.0,51.0,48.0,60.0,30.0,45.0,60.0,30.0,38.0,55.0,40.0,20.0,48.0,50.0,39.0,45.0,37.0,45.0,65.0,30.0,35.0,50.0,65.0,50.0,50.0,46.0,51.0,28.0,41.0,27.0,38.0,39.0,50.0,35.0,28.0,52.0,50.0,52.0,25.0,23.0,45.0,60.0,32.0,38.0,55.0,38.0,25.0,37.0,39.0,38.0,25.0,49.0,44.0,55.0,42.0,57.0,28.0,42.0,20.0,40.0,34.0,80.0,45.0,35.0,39.0,39.0,48.0,29.0,43.0,90.0,45.0,35.0,65.0,58.0,39.0,65.0,43.0,35.0,38.0,65.0,41.0,40.0,40.0,36.0,40.0,45.0,30.0,74.0,37.0,44.0,50.0,60.0,70.0,39.0,40.0,67.0,50.0,32.0,49.0,70.0,29.0,37.0,69.0,55.0,45.0,34.0,20.0,35.0,47.0,45.0,50.0,20.0,45.0,36.0,35.0,43.0,34.0,35.0,43.0,44.0,65.0,35.0,44.0,60.0,60.0,54.0,90.0,35.0,60.0,52.0,40.0,35.0,43.0,73.0,36.0,39.0,42.0,28.0,22.0,41.0,45.0,28.0,65.0,25.0,72.0,41.0,60.0,40.0,70.0,65.0,55.0,35.0,34.0,43.0,57.0,64.0,45.0,50.0,50.0,60.0,35.0,55.0,54.0,50.0,47.0,38.0,38.0,53.0,54.0,50.0,49.0,40.0,25.0,42.0,26.0,50.0,38.0,56.0,42.0,65.0,40.0,50.0,58.0,41.0,44.0,55.0,51.0,43.0,55.0,31.0,23.0,36.0,44.0,38.0,53.0,45.0,40.0,45.0,55.0,40.0,45.0,55.0,35.0,20.0,22.0,40.0,80.0,70.0,35.0,39.0,44.0,37.0,45.0,35.0,65.0,55.0,60.0,43.0,42.0,65.0,55.0,34.0,22.0,41.0,47.0,59.0,48.0,80.0,53.0,50.0,40.0,40.0,60.0,38.0,30.0,55.0,45.0,25.0,35.0,50.0,40.0,36.0,41.0,65.0,70.0,74.0,55.0,55.0,51.0,50.0,55.0,36.0,56.0,28.0,43.0,50.0,43.0,45.0,42.0,41.0,50.0,24.0,75.0,50.0,78.0,80.0,40.0,20.0,46.0,45.0,49.0,48.0,39.0,50.0,30.0,38.0,58.0,48.0,28.0,47.0,56.0,34.0,25.0,33.0,33.0,37.0,47.0,65.0,30.0,14.0,34.0,55.0,43.0,58.0,85.0,32.0,46.0,40.0,71.0,59.0,38.0,45.0,21.0,53.0,40.0,40.0,27.0,54.0,32.0,55.0,38.0,39.0,60.0,29.0,60.0,40.0,61.0,38.0,18.0,59.0,29.0,45.0,48.0,45.0,44.0,65.0,28.0,40.0,50.0,38.0,45.0,60.0,37.0,45.0,60.0,44.0,15.0,50.0,52.0,39.0,55.0,51.0,39.0,33.0,46.0,30.0,38.0,70.0,40.0,45.0,34.0,60.0,40.0,41.0,48.0,40.0,15.0,65.0,55.0,16.0,58.0,44.0,64.0,36.0,35.0,65.0,29.0,67.0,55.0,55.0,50.0,75.0,46.0,55.0,45.0,48.0,45.0,40.0,34.0,90.0,35.0,38.0,49.0,45.0,25.0,36.0,68.0,60.0,50.0,50.0,55.0,50.0,26.0,25.0,50.0,33.0,19.0,44.0,35.0,58.0,40.0,65.0,75.0,60.0,40.0,25.0,36.0,62.0,24.0,19.0,45.0,55.0,30.0,68.0,35.0,43.0,18.0,27.0,37.0,55.0,55.0,47.0,40.0,54.0,72.0,60.0,32.0,45.0,30.0,35.0,20.0,64.0,30.0,44.0,50.0,49.0,52.0,38.0,28.0,54.0,38.0,45.0,24.0,55.0,34.0,69.0,26.0,30.0,41.0,51.0,30.0,60.0,75.0,35.0,27.0,65.0,40.0,45.0,39.0,52.0,29.0,47.0,55.0,45.0,55.0,53.0,51.0,60.0,56.0,46.0,33.0,42.0,33.0,60.0,35.0,65.0,45.0,24.0,37.0,43.0,62.0,50.0,55.0,45.0,90.0,53.0,65.0,50.0,60.0,15.0,35.0,55.0,33.0,46.0,50.0,50.0,55.0,64.0,18.0,33.0,34.0,63.0,43.0,38.0,48.0,60.0,55.0,28.0,36.0,29.0,41.0,44.0,35.0,39.0,59.0,37.0,75.0,50.0,85.0,60.0,52.0,80.0,65.0,50.0,20.0,44.0,35.0,20.0,67.0,32.0,60.0,33.0,54.0,38.0,66.0,49.0,43.0,55.0,64.0,70.0,70.0,65.0,60.0,56.0,60.0,38.0,56.0,53.0,50.0,41.0,30.0,70.0,20.0,65.0,46.0,40.0,42.0,52.0,68.0,55.0,35.0,35.0,33.0,32.0,45.0,70.0,50.0,55.0,39.0,45.0,30.0,45.0,28.0,43.0,67.0,55.0,50.0,33.0,45.0,47.0,45.0,41.0,66.0,50.0,15.0,65.0,32.0,55.0,14.0,53.0,60.0,48.0,45.0,55.0,32.0,49.0,50.0,50.0,49.0,60.0,60.0,42.0,37.0,45.0,36.0,75.0,45.0,50.0,71.0,39.0,20.0,41.0,50.0,47.0,65.0,64.0,37.0,80.0,49.0,30.0,40.0,52.0,50.0,60.0,40.0,60.0,55.0,41.0,50.0,37.0,50.0,45.0,65.0,35.0,35.0,50.0,30.0,17.0,36.0,55.0,44.0,44.0,47.0,39.0,45.0,32.0,49.0,36.0,64.0,51.0,31.0,45.0,56.0,33.0,18.0,49.0,45.0,65.0,35.0,35.0,10.0,30.0,20.0,50.0,50.0,66.0,55.0,52.0,49.0,45.0,55.0,30.0,40.0,45.0,29.0,51.0,29.0,70.0,38.0,46.0,36.0,55.0,44.0,48.0,59.0,41.0,48.0,55.0,72.0,55.0,72.0,47.0,40.0,38.0,37.0,75.0,60.0,54.0,56.0,40.0,40.0,55.0,59.0,45.0,31.0,55.0,49.0,41.0,52.0,55.0,40.0,48.0,60.0,42.0,77.0,44.0,34.0,29.0,44.0,25.0,47.0,60.0,50.0,55.0,30.0,38.0,35.0,54.0,50.0,55.0,19.0,30.0,40.0,68.0,55.0,35.0,80.0,42.0,70.0,45.0,55.0,28.0,42.0,15.0,65.0,36.0,35.0,35.0,35.0,45.0,70.0,25.0,46.0,55.0,22.0,81.0,45.0,64.0,43.0,35.0,42.0,34.0,34.0,65.0,33.0,50.0,40.0,55.0,50.0,44.0,85.0,75.0,35.0,55.0,35.0,50.0,45.0,69.0,40.0,33.0,38.0,62.0,45.0,45.0,55.0,45.0,35.0,55.0,18.0,39.0,55.0,35.0,29.0,58.0,30.0,23.0,31.0,70.0,65.0,40.0,46.0,52.0,50.0,53.0,43.0,51.0,41.0,29.0,32.0,11.0,26.0,45.0,45.0,60.0,68.0,40.0,56.0,51.0,75.0,40.0,45.0,18.0,49.0,41.0,36.0,32.0,40.0,35.0,35.0,49.0,16.0,41.0,63.0,37.0,78.0,28.0,59.0,32.0,40.0,43.0,56.0,55.0,27.0,41.0,55.0,28.0,55.0,49.0,41.0,60.0,48.0,45.0,50.0,26.0,47.0,71.0,48.0,32.0,40.0,22.0,58.0,47.0,32.0,29.0,35.0,40.0,30.0,20.0,55.0,65.0,7.0,39.0,49.0,46.0,74.0,66.0,45.0,36.0,50.0,48.0,24.0,50.0,40.0,47.0,60.0,50.0,36.0,45.0,50.0,50.0,41.0,55.0,54.0,55.0,71.0,51.0,60.0,25.0,34.0,49.0,55.0,43.0,15.0,50.0,70.0,35.0,50.0,30.0,41.0,80.0,45.0,45.0,45.0,60.0,28.0,45.0,27.0,60.0,50.0,12.0,80.0,58.0,33.0,39.0,35.0,53.0,35.0,65.0,34.0,47.0,55.0,41.0,55.0,41.0,65.0,27.0,40.0,59.0,50.0,30.0,60.0,90.0,42.0,55.0,55.0,44.0,50.0,43.0,47.0,50.0,62.0,45.0,55.0,55.0,43.0,55.0,35.0,39.0,29.0,50.0,30.0,54.0,35.0,41.0,82.0,61.0,60.0,16.0,55.0,56.0,24.0,28.0,43.0,38.0,69.0,45.0,45.0,34.0,65.0,38.0,33.0,60.0,45.0,45.0,42.0,42.0,68.0,49.0,32.0,34.0,28.0,65.0,60.0,25.0,47.0,55.0,68.0,46.0,45.0,56.0,49.0,55.0,48.0,32.0,34.0,36.0,40.0,45.0,45.0,60.0,35.0,35.0,45.0,44.0,53.0,32.0,34.0,35.0,29.0,51.0,54.0,52.0,23.0,19.0,35.0,50.0,49.0,39.0,53.0,25.0,55.0,45.0,44.0,50.0,50.0,35.0,40.0,55.0,34.0,45.0,36.0,31.0,20.0,51.0,46.0,50.0,30.0,50.0,70.0,43.0,28.0,50.0,18.0,42.0,50.0,50.0,36.0,32.0,55.0,15.0,41.0,42.0,65.0,60.0,40.0,23.0,50.0,50.0,48.0,39.0,19.0,15.0,43.0,30.0,56.0,55.0,45.0,33.0,29.0,18.0,45.0,77.0,34.0,62.0,49.0,27.0,34.0,47.0,40.0,95.0,49.0,49.0,60.0,49.0,25.0,64.0,55.0,45.0,35.0,50.0,60.0,33.0,60.0,55.0,30.0,18.0,75.0,27.0,110.0,65.0,55.0,19.0,44.0,29.0,23.0,60.0,45.0,27.0,48.0,36.0,65.0,50.0,40.0,34.0,17.0,52.0,45.0,63.0,58.0,50.0,32.0,50.0,50.0,73.0,33.0,49.0,25.0,20.0,13.0,55.0,51.0,47.0,38.0,38.0,54.0,22.0,51.0,55.0,75.0,39.0,14.0,85.0,60.0,62.0,80.0,60.0,62.0,26.0,62.0,48.0,29.0,35.0,40.0,37.0,20.0,41.0,61.0,39.0,55.0,25.0,35.0,19.0,14.0,85.0,65.0,60.0,62.0,37.0,60.0,23.0,46.0,40.0,15.0,55.0,45.0,59.0,40.0,55.0,27.0,72.0,75.0,36.0,19.0,70.0,53.0,60.0,58.0,35.0,51.0,35.0,55.0,55.0,28.0,40.0,33.0,33.0,42.0,28.0,69.0,59.0,40.0,45.0,60.0,50.0,41.0,45.0,50.0,29.0,45.0,32.0,42.0,14.0,34.0,40.0,48.0,45.0,65.0,20.0,22.0,38.0,43.0,40.0,15.0,35.0,37.0,53.0,20.0,33.0,57.0,29.0,35.0,40.0,33.0,20.0,39.0,54.0,41.0,45.0,55.0,45.0,14.0,56.0,51.0,70.0,55.0,45.0,65.0,35.0,47.0,41.0,46.0,65.0,24.0,30.0,45.0,53.0,51.0,53.0,28.0,55.0,55.0,20.0,53.0,32.0,45.0,33.0,47.0,43.0,81.0,40.0,25.0,39.0,27.0,67.0,49.0,40.0,60.0,36.0,37.0,40.0,50.0,86.0,23.0,42.0,46.0,48.0,27.0,27.0,23.0,49.0,44.0,45.0,28.0,55.0,42.0,65.0,44.0,20.0,27.0,65.0,55.0,68.0,53.0,80.0,44.0,50.0,40.0,48.0,55.0,50.0,51.0,52.0,55.0,60.0,27.0,55.0,10.0,19.0,38.0,75.0,65.0,35.0,36.0,36.0,32.0,15.0,55.0,55.0,65.0,35.0,58.0,35.0,32.0,70.0,15.0,55.0,53.0,60.0,30.0,54.0,25.0,60.0,35.0,24.0,54.0,42.0,45.0,36.0,85.0,24.0,40.0,25.0,37.0,44.0,39.0,28.0,25.0,45.0,43.0,41.0,56.0,26.0,40.0,33.0,67.0,75.0,20.0,28.0,45.0,60.0,56.0,60.0,40.0,40.0,25.0,48.0,19.0,65.0,40.0,20.0,60.0,36.0,50.0,23.0,68.0,53.0,50.0,70.0,45.0,35.0,60.0,53.0,41.0,33.0,45.0,55.0,59.0,30.0,29.0,49.0,69.0,60.0,46.0,82.0,63.0,41.0,47.0,14.0,40.0,38.0,55.0,26.0,60.0,30.0,45.0,55.0,26.0,35.0,55.0,30.0,65.0,23.0,57.0,37.0,34.0,40.0,60.0,38.0,50.0,59.0,45.0,50.0,25.0,55.0,52.0,36.0,29.0,65.0,32.0,60.0,51.0,70.0,75.0,62.0,30.0,81.0,36.0,32.0,37.0,19.0,36.0,55.0,41.0,60.0,60.0,32.0,43.0,38.0,44.0,54.0,59.0,45.0,25.0,50.0,70.0,39.0,55.0,65.0,60.0,60.0,44.0,44.0,55.0,52.0,70.0,62.0,72.0,46.0,37.0,38.0,34.0,40.0,39.0,45.0,29.0,34.0,65.0,40.0,55.0,38.0,49.0,27.0,42.0,27.0,42.0,45.0,54.0,29.0,58.0,26.0,45.0,48.0,35.0,46.0,58.0,60.0,30.0,29.0,53.0,35.0,47.0,54.0,49.0,49.0,33.0,67.0,49.0,50.0,32.0,20.0,28.0,18.0,30.0,32.0,62.0,80.0,33.0,30.0,40.0,38.0,50.0,30.0,42.0,50.0,72.0,51.0,77.0,74.0,45.0,47.0,35.0,50.0,45.0,35.0,39.0,32.0,50.0,39.0,40.0,20.0,53.0,75.0,52.0,25.0,68.0,33.0,46.0,45.0,53.0,65.0,34.0,50.0,24.0,48.0,50.0,45.0,60.0,47.0,34.0,34.0,67.0,33.0,55.0,39.0,25.0,45.0,50.0,60.0,25.0,68.0,34.0,60.0,40.0,32.0,60.0,44.0,37.0,50.0,29.0,53.0,32.0,30.0,40.0,44.0,37.0,28.0,42.0,64.0,43.0,27.0,40.0,61.0,42.0,35.0,38.0,44.0,80.0,45.0,36.0,55.0,41.0,54.0,30.0,30.0,42.0,62.0,14.0,39.0,36.0,55.0,35.0,44.0,24.0,55.0,60.0,65.0,55.0,52.0,35.0,28.0,50.0,54.0,30.0,22.0,66.0,46.0,55.0,20.0,20.0,41.0,31.0,15.0,35.0,60.0,85.0,41.0,55.0,60.0,45.0,33.0,60.0,25.0,30.0,47.0,75.0,55.0,55.0,39.0,37.0,57.0,70.0,50.0,45.0,14.0,45.0,30.0,46.0,55.0,54.0,29.0,35.0,55.0,51.0,62.0,37.0,59.0,38.0,46.0,58.0,23.0,34.0,42.0,30.0,34.0,25.0,70.0,40.0,55.0,29.0,35.0,65.0,64.0,53.0,29.0,48.0,55.0,29.0,39.0,45.0,30.0,45.0,52.0,50.0,38.0,29.0,23.0,55.0,50.0,50.0,59.0,65.0,35.0,32.0,45.0,50.0,40.0,40.0,27.0,50.0,20.0,50.0,30.0,34.0,70.0,56.0,43.0,46.0,37.0,41.0,32.0,23.0,52.0,38.0,41.0,42.0,26.0,41.0,55.0,45.0,20.0,66.0,26.0,36.0,39.0,50.0,50.0,43.0,16.0,28.0,62.0,35.0,65.0,47.0,40.0,54.0,51.0,54.0,37.0,27.0,44.0,40.0,38.0,46.0,48.0,65.0,39.0,40.0,56.0,10.0,86.0,25.0,58.0,50.0,60.0,46.0,29.0,40.0,60.0,60.0,60.0,50.0,45.0,42.0,50.0,53.0,60.0,35.0,58.0,37.0,28.0,41.0,29.0,45.0,49.0,55.0,33.0,58.0,33.0,40.0,48.0,50.0,40.0,50.0,33.0,35.0,54.0,36.0,50.0,39.0,40.0,45.0,65.0,45.0,47.0,20.0,28.0,64.0,70.0,50.0,40.0,55.0,50.0,75.0,50.0,55.0,50.0,23.0,55.0,65.0,36.0,44.0,59.0,30.0,50.0,49.0,43.0,65.0,34.0,45.0,47.0,65.0,19.0,11.0,45.0,35.0,38.0,60.0,67.0,34.0,26.0,60.0,55.0,36.0,23.0,45.0,55.0,55.0,58.0,61.0,40.0,54.0,59.0,32.0,38.0,35.0,33.0,15.0,65.0,34.0,9.0,44.0,35.0,50.0,19.0,30.0,55.0,74.0,55.0,45.0,26.0,26.0,45.0,15.0,60.0,25.0,20.0,45.0,59.0,20.0,61.0,35.0,36.0,45.0,36.0,62.0,27.0,41.0,25.0,45.0,40.0,30.0,54.0,50.0,44.0,32.0,63.0,20.0,47.0,45.0,20.0,75.0,68.0,54.0,41.0,60.0,60.0,54.0,55.0,30.0,41.0,47.0,42.0,30.0,35.0,41.0,59.0,34.0,52.0,45.0,48.0,35.0,40.0,20.0,35.0,20.0,61.0,36.0,44.0,50.0,60.0,40.0,64.0,45.0,60.0,48.0,30.0,38.0,55.0,53.0,38.0,58.0,70.0,70.0,45.0,53.0,25.0,50.0,35.0,25.0,29.0,24.0,55.0,19.0,39.0,20.0,46.0,32.0,60.0,31.0,47.0,43.0,45.0,43.0,30.0,40.0,46.0,60.0,50.0,36.0,30.0,43.0,50.0,55.0,36.0,45.0,41.0,60.0,36.0,39.0,78.0,45.0,34.0,41.0,28.0,80.0,36.0,55.0,24.0,57.0,55.0,65.0,38.0,30.0,31.0,55.0,42.0,51.0,35.0,41.0,32.0,32.0,27.0,53.0,35.0,32.0,53.0,46.0,19.0,50.0,47.0,50.0,55.0,58.0,38.0,46.0,41.0,65.0,56.0,40.0,28.0,40.0,50.0,45.0,47.0,39.0,25.0,50.0,45.0,35.0,27.0,60.0,37.0,53.0,28.0,40.0,60.0,45.0,62.0,32.0,24.0,57.0,28.0,62.0,50.0,43.0,40.0,49.0,23.0,33.0,37.0,20.0,38.0,57.0,47.0,18.0,69.0,31.0,36.0,53.0,37.0,28.0,55.0,19.0,63.0,50.0,55.0,40.0,50.0,33.0,54.0,30.0,45.0,47.0,66.0,62.0,60.0,75.0,40.0,19.0,50.0,49.0,60.0,46.0,80.0,59.0,52.0,68.0,41.0,55.0,60.0,36.0,42.0,30.0,60.0,39.0,50.0,50.0,30.0,39.0,33.0,25.0,30.0,17.0,64.0,48.0,47.0,50.0,38.0,34.0,35.0,50.0,42.0,19.0,55.0,20.0,71.0,38.0,27.0,55.0,23.0,38.0,42.0,50.0,55.0,65.0,50.0,29.0,40.0,66.0,55.0,30.0,29.0,50.0,70.0,46.0,70.0,56.0,74.0,52.0,50.0,49.0,24.0,75.0,75.0,47.0,50.0,90.0,95.0,35.0,68.0,40.0,20.0,37.0,39.0,33.0,52.0,44.0,50.0,47.0,20.0,46.0,48.0,40.0,43.0,48.0,37.0,43.0,45.0,37.0,35.0,30.0,85.0,43.0,40.0,59.0,63.0,50.0,64.0,49.0,50.0,62.0,40.0,38.0,41.0,45.0,28.0,50.0,40.0,49.0,55.0,40.0,15.0,37.0,23.0,25.0,34.0,42.0,50.0,60.0,63.0,42.0,44.0,36.0,50.0,40.0,36.0,43.0,37.0,32.0,33.0,65.0,33.0,24.0,60.0,50.0,55.0,60.0,44.0,75.0,49.0,49.0,45.0,43.0,34.0,65.0,40.0,40.0,58.0,40.0,73.0,55.0,23.0,25.0,13.0,38.0,35.0,65.0,37.0,52.0,43.0,40.0,60.0,34.0,36.0,65.0,50.0,42.0,44.0,60.0,17.0,55.0,51.0,50.0,22.0,56.0,36.0,67.0,65.0,58.0,65.0,19.0,105.0,51.0,27.0,55.0,56.0,20.0,55.0,60.0,58.0,56.0,55.0,58.0,65.0,52.0,37.0,30.0,50.0,8.0,60.0,40.0,70.0,70.0,61.0,30.0,30.0,47.0,33.0,25.0,42.0,75.0,38.0,27.0,54.0,50.0,54.0,35.0,33.0,48.0,11.0,16.0,100.0,59.0,56.0,32.0,34.0,49.0,36.0,36.0,29.0,62.0,23.0,35.0,60.0,43.0,44.0,85.0,48.0,40.0,56.0,59.0,54.0,23.0,35.0,59.0,55.0,46.0,50.0,77.0,70.0,31.0,51.0,51.0,65.0,25.0,51.0,35.0,53.0,28.0,35.0,55.0,32.0,52.0,28.0,35.0,23.0,29.0,41.0,48.0,30.0,21.0,28.0,42.0,50.0,49.0,60.0,41.0,28.0,55.0,45.0,40.0,50.0,40.0,70.0,44.0,40.0,54.0,45.0,23.0,35.0,32.0,27.0,74.0,27.0,34.0,25.0,50.0,65.0,26.0,44.0,27.0,45.0,51.0,55.0,65.0,33.0,45.0,49.0,42.0,54.0,45.0,45.0,50.0,50.0,41.0,35.0,38.0,39.0,41.0,39.0,50.0,46.0,44.0,35.0,30.0,47.0,80.0,45.0,63.0,35.0,40.0,32.0,46.0,59.0,30.0,50.0,70.0,95.0,24.0,39.0,60.0,50.0,52.0,38.0,32.0,35.0,37.0,35.0,44.0,40.0,38.0,33.0,51.0,41.0,60.0,60.0,36.0,56.0,41.0,41.0,23.0,10.0,50.0,44.0,35.0,69.0,40.0,60.0,36.0,28.0,45.0,50.0,45.0,36.0,28.0,65.0,30.0,32.0,21.0,29.0,50.0,42.0,52.0,53.0,36.0,55.0,34.0,60.0,30.0,90.0,60.0,37.0,57.0,50.0,36.0,27.0,47.0,11.0,36.0,55.0,51.0,61.0,54.0,42.0,34.0,46.0,55.0,52.0,30.0,41.0,51.0,25.0,45.0,19.0,56.0,80.0,36.0,54.0,55.0,65.0,60.0,47.0,45.0,44.0,35.0,83.0,70.0,34.0,51.0,50.0,55.0,39.0,63.0,45.0,50.0,50.0,40.0,50.0,40.0,45.0,32.0,38.0,40.0,18.0,60.0,25.0,72.0,65.0,60.0,52.0,65.0,82.0,54.0,70.0,14.0,65.0,41.0,35.0,38.0,36.0,48.0,38.0,85.0,45.0,43.0,55.0,45.0,59.0,36.0,70.0,35.0,45.0,67.0,70.0,40.0,60.0,40.0,59.0,56.0,30.0,33.0,44.0,55.0,64.0,51.0,32.0,90.0,40.0,45.0,14.0,60.0,35.0,36.0,50.0,35.0,70.0,55.0,55.0,45.0,65.0,60.0,29.0,38.0,52.0,40.0,28.0,34.0,52.0,46.0,29.0,26.0,47.0,55.0,60.0,60.0,70.0,20.0,59.0,36.0,38.0,50.0,51.0,80.0,32.0,29.0,88.0,70.0,43.0,45.0,50.0,60.0,54.0,70.0,19.0,55.0,39.0,45.0,35.0,70.0,20.0,52.0,55.0,42.0,45.0,75.0,34.0,65.0,30.0,32.0,29.0,40.0,42.0,33.0,55.0,25.0,56.0,27.0,29.0,50.0,23.0,55.0,25.0,55.0,45.0,50.0,16.0,14.0,15.0,53.0,80.0,27.0,39.0,30.0,46.0,32.0,45.0,69.0,40.0,50.0,46.0,45.0,45.0,46.0,48.0,35.0,22.0,50.0,29.0,46.0,37.0,41.0,49.0,45.0,43.0,50.0,41.0,41.0,27.0,55.0,18.0,55.0,47.0,33.0,39.0,50.0,42.0,38.0,30.0,60.0,42.0,87.0,55.0,31.0,60.0,47.0,50.0,45.0,30.0,45.0,45.0,25.0,39.0,62.0,14.0,65.0,45.0,50.0,39.0,25.0,24.0,16.0,28.0,45.0,40.0,20.0,35.0,70.0,42.0,44.0,40.0,60.0,30.0,65.0,45.0,55.0,35.0,43.0,43.0,42.0,70.0,48.0,43.0,30.0,28.0,60.0,49.0,70.0,49.0,25.0,40.0,35.0,100.0,36.0,54.0,50.0,29.0,52.0,27.0,65.0,70.0,55.0,45.0,35.0,36.0,33.0,47.0,40.0,17.0,37.0,50.0,50.0,56.0,55.0,55.0,41.0,54.0,24.0,33.0,50.0,55.0,44.0,78.0,55.0,33.0,30.0,43.0,45.0,24.0,35.0,40.0,49.0,55.0,50.0,40.0,53.0,34.0,67.0,54.0,57.0,66.0,40.0,65.0,47.0,40.0,37.0,47.0,50.0,35.0,71.0,45.0,60.0,54.0,50.0,51.0,75.0,65.0,41.0,52.0,20.0,35.0,43.0,41.0,41.0,39.0,50.0,60.0,47.0,36.0,45.0,40.0,38.0,26.0,39.0,40.0,59.0,44.0,33.0,30.0,56.0,29.0,40.0,65.0,30.0,35.0,45.0,39.0,50.0,44.0,19.0,43.0,30.0,38.0,64.0,25.0,54.0,70.0,44.0,44.0,50.0,55.0,50.0,45.0,14.0,44.0,30.0,36.0,34.0,78.0,40.0,46.0,65.0,24.0,53.0,33.0,59.0,50.0,52.0,32.0,70.0,48.0,45.0,50.0,40.0,36.0,45.0,60.0,14.0,28.0,53.0,45.0,36.0,49.0,23.0,35.0,23.0,35.0,70.0,45.0,27.0,45.0,41.0,40.0,65.0,45.0,33.0,37.0,24.0,54.0,55.0,15.0,39.0,38.0,25.0,57.0,40.0,67.0,33.0,60.0,64.0,35.0,68.0,34.0,10.0,42.0,31.0,60.0,47.0,44.0,23.0,59.0,55.0,75.0,25.0,20.0,57.0,35.0,41.0,15.0,24.0,45.0,68.0,54.0,55.0,55.0,56.0,30.0,81.0,45.0,25.0,43.0,47.0,51.0,53.0,68.0,40.0,70.0,30.0,42.0,39.0,40.0,35.0,51.0,45.0,35.0,30.0,30.0,50.0,52.0,40.0,55.0,49.0,30.0,24.0,46.0,53.0,59.0,40.0,20.0,45.0,41.0,40.0,35.0,41.0,49.0,40.0,65.0,30.0,45.0,39.0,55.0,55.0,50.0,42.0,30.0,34.0,43.0,51.0,47.0,45.0,36.0,30.0,40.0,35.0,30.0,60.0,23.0,60.0,47.0,29.0,65.0,36.0,50.0,31.0,40.0,69.0,39.0,30.0,34.0,55.0,56.0,65.0,25.0,72.0,45.0,55.0,35.0,36.0,50.0,32.0,29.0,47.0,65.0,33.0,39.0,55.0,45.0,40.0,50.0,56.0,55.0,20.0,51.0,74.0,55.0,29.0,64.0,58.0,62.0,60.0,60.0,46.0,76.0,37.0,48.0,67.0,45.0,40.0,56.0,50.0,45.0,49.0,60.0,45.0,66.0,55.0,51.0,62.0,85.0,56.0,14.0,34.0,60.0,30.0,43.0,20.0,60.0,38.0,78.0,40.0,50.0,28.0,55.0,42.0,34.0,45.0,50.0,48.0,59.0,41.0,50.0,20.0,15.0,45.0,41.0,55.0,56.0,40.0,75.0,44.0,45.0,55.0,49.0,58.0,55.0,48.0,50.0,70.0,29.0,27.0,40.0,57.0,30.0,55.0,36.0,28.0,40.0,46.0,35.0,75.0,25.0,35.0,45.0,38.0,25.0,25.0,65.0,36.0,50.0,24.0,53.0,45.0,58.0,38.0,53.0,33.0,24.0,40.0,35.0,19.0,29.0,37.0,32.0,26.0,30.0,60.0,60.0,56.0,41.0,51.0,29.0,54.0,64.0,35.0,80.0,53.0,50.0,40.0,60.0,48.0,48.0,49.0,55.0,33.0,60.0,55.0,41.0,50.0,50.0,51.0,50.0,32.0,75.0,33.0,55.0,28.0,38.0,34.0,25.0,90.0,39.0,44.0,41.0,50.0,27.0,65.0,55.0,40.0,30.0,68.0,40.0,36.0,40.0,51.0,64.0,50.0,45.0,50.0,50.0,25.0,50.0,51.0,33.0,44.0,41.0,27.0,45.0,70.0,14.0,50.0,44.0,25.0,46.0,34.0,55.0,40.0,45.0,33.0,33.0,41.0,28.0,50.0,52.0,34.0,50.0,25.0,39.0,55.0,35.0,35.0,50.0,60.0,29.0,48.0,40.0,50.0,50.0,75.0,55.0,39.0,45.0,40.0,60.0,30.0,55.0,46.0,45.0,50.0,39.0,60.0,54.0,53.0,50.0,57.0,45.0,50.0,43.0,60.0,35.0,49.0,31.0,50.0,70.0,43.0,39.0,40.0,60.0,36.0,45.0,32.0,60.0,35.0,34.0,59.0,31.0,45.0,53.0,69.0,65.0,40.0,18.0,38.0,76.0,32.0,37.0,70.0,18.0,29.0,46.0,30.0,44.0,45.0,50.0,55.0,20.0,40.0,59.0,9.0,70.0,56.0,28.0,55.0,40.0,40.0,51.0,55.0,28.0,25.0,33.0,28.0,45.0,40.0,35.0,110.0,50.0,47.0,50.0,50.0,40.0,63.0,70.0,36.0,28.0,60.0,26.0,20.0,54.0,50.0,44.0,32.0,50.0,55.0,19.0,40.0,52.0,53.0,15.0,57.0,53.0,32.0,34.0,50.0,47.0,15.0,35.0,47.0,36.0,58.0,60.0,30.0,70.0,49.0,74.0,55.0,38.0,54.0,46.0,26.0,51.0,20.0,22.0,65.0,45.0,25.0,32.0,22.0,30.0,70.0,55.0,65.0,20.0,38.0,40.0,50.0,40.0,35.0,46.0,40.0,20.0,41.0,40.0,45.0,48.0,47.0,36.0,39.0,50.0,47.0,41.0,28.0,25.0,60.0,75.0,60.0,29.0,59.0,44.0,34.0,65.0,85.0,62.0,33.0,38.0,60.0,49.0,59.0,34.0,60.0,25.0,65.0,20.0,55.0,41.0,20.0,25.0,50.0,41.0,29.0,50.0,60.0,57.0,37.0,37.0,60.0,31.0,46.0,59.0,60.0,29.0,58.0,39.0,39.0,25.0,49.0,35.0,40.0,25.0,55.0,20.0,40.0,35.0,68.0,45.0,51.0,30.0,40.0,45.0,60.0,45.0,40.0,62.0,43.0,60.0,60.0,25.0,37.0,55.0,25.0,20.0,65.0,36.0,44.0,64.0,45.0,45.0,47.0,34.0,32.0,50.0,62.0,19.0,65.0,30.0,54.0,60.0,36.0,21.0,49.0,55.0,26.0,30.0,35.0,33.0,50.0,65.0,46.0,83.0,50.0,50.0,30.0,55.0,50.0,35.0,59.0,65.0,43.0,35.0,25.0,50.0,36.0,32.0,40.0,39.0,46.0,54.0,60.0,58.0,48.0,35.0,62.0,50.0,45.0,54.0,70.0,50.0,24.0,47.0,59.0,33.0,40.0,53.0,24.0,45.0,23.0,52.0,34.0,40.0,31.0,33.0,46.0,24.0,40.0,45.0,50.0,45.0,55.0,51.0,35.0,40.0,40.0,16.0,69.0,47.0,55.0,20.0,35.0,41.0,56.0,27.0,69.0,25.0,54.0,35.0,53.0,50.0,64.0,40.0,41.0,59.0,64.0,60.0,40.0,35.0,60.0,46.0,45.0,37.0,25.0,28.0,55.0,55.0,65.0,32.0,46.0,48.0,36.0,35.0,23.0,41.0,65.0,31.0,45.0,14.0,40.0,49.0,15.0,34.0,60.0,46.0,37.0,40.0,45.0,50.0,44.0,64.0,24.0,45.0,50.0,47.0,51.0,55.0,27.0,58.0,30.0,60.0,7.0,47.0,60.0,45.0,45.0,60.0,40.0,30.0,23.0,20.0,60.0,42.0,50.0,32.0,37.0,29.0,52.0,30.0,30.0,32.0,56.0,39.0,14.0,44.0,37.0,46.0,23.0,60.0,58.0,46.0,40.0,60.0,55.0,70.0,15.0,47.0,40.0,38.0,51.0,23.0,48.0,45.0,45.0,35.0,65.0,49.0,35.0,51.0,14.0,55.0,33.0,50.0,29.0,55.0,13.0,40.0,52.0,60.0,50.0,40.0,54.0,20.0,40.0,23.0,28.0,50.0,54.0,46.0,68.0,35.0,45.0,54.0,59.0,49.0,65.0,40.0,31.0,38.0,33.0,25.0,90.0,60.0,55.0,45.0,40.0,20.0,41.0,45.0,41.0,55.0,58.0,39.0,95.0,55.0,33.0,65.0,95.0,36.0,35.0,43.0,27.0,49.0,50.0,45.0,25.0,44.0,40.0,45.0,75.0,50.0,30.0,50.0,24.0,24.0,25.0,20.0,50.0,40.0,45.0,50.0,45.0,40.0,49.0,49.0,9.0,24.0,35.0,54.0,55.0,50.0,50.0,41.0,45.0,68.0,35.0,60.0,50.0,35.0,46.0,54.0,53.0,24.0,35.0,42.0,65.0,30.0,29.0,40.0,50.0,44.0,32.0,26.0,57.0,42.0,27.0,28.0,30.0,55.0,35.0,74.0,65.0,60.0,46.0,30.0,62.0,25.0,61.0,45.0,40.0,45.0,39.0,62.0,32.0,25.0,44.0,39.0,60.0,45.0,41.0,45.0,65.0,39.0,51.0,38.0,43.0,38.0,38.0,32.0,38.0,30.0,34.0,56.0,39.0,20.0,45.0,34.0,56.0,26.0,42.0,55.0,51.0,24.0,40.0,49.0,20.0,38.0,38.0,62.0,35.0,72.0,33.0,60.0,41.0,70.0,31.0,43.0,32.0,37.0,46.0,42.0,34.0],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Price per Unit\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"count\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Distribution of PPU\"},\"barmode\":\"relative\"},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('1688bb91-31e6-4f54-9010-f71488f37f50');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "bc90132e",
        "execution_start": 1708811824819,
        "execution_millis": 284,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "24686246196843748fa6b57302980f33",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "id": "pHSQ3ibBId65",
        "outputId": "275d627a-e988-449b-8197-08cc1474d6f8"
      },
      "source": [
        "sns.boxplot(df2[\"Total Sales\"])\n",
        "sns.set(rc={'figure.figsize':(17, 17)})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1170x827 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "4a82ccb3a2b84b1da34374776b4b0aa7",
        "deepnote_cell_type": "text-cell-h1",
        "id": "36lXpQYIId65"
      },
      "source": [
        "# Линейная Регрессия на ненормализированных данных"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "4ba2256a",
        "execution_start": 1708811825117,
        "execution_millis": 251,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "ef041f23bf804555bc958c799282e154",
        "deepnote_cell_type": "code",
        "id": "w5kJ6F4uId66"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import StandardScaler\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "caa55e2e",
        "execution_start": 1708811825232,
        "execution_millis": 136,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "2997f81febda4e32b6613b801a7c9ff7",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "-7bLObTaId66",
        "outputId": "200143b7-1eca-4933-c07e-54d55b224bc5"
      },
      "source": [
        "df2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Price per Unit  Total Sales  Region_Midwest  Region_Northeast  \\\n",
              "0               35.0      21700.0               1                 0   \n",
              "1               25.0      18000.0               0                 0   \n",
              "2               61.0       7320.0               0                 0   \n",
              "3               43.0       1505.0               1                 0   \n",
              "4               55.0      45375.0               0                 0   \n",
              "...              ...          ...             ...               ...   \n",
              "9643            32.0       2176.0               1                 0   \n",
              "9644            37.0        259.0               1                 0   \n",
              "9645            46.0       5750.0               0                 0   \n",
              "9646            42.0       8820.0               0                 0   \n",
              "9647            34.0       2618.0               0                 1   \n",
              "\n",
              "      Region_South  Region_Southeast  Region_West  Retailer_Amazon  \\\n",
              "0                0                 0            0                0   \n",
              "1                1                 0            0                0   \n",
              "2                0                 0            1                0   \n",
              "3                0                 0            0                0   \n",
              "4                0                 1            0                0   \n",
              "...            ...               ...          ...              ...   \n",
              "9643             0                 0            0                0   \n",
              "9644             0                 0            0                0   \n",
              "9645             1                 0            0                0   \n",
              "9646             0                 1            0                0   \n",
              "9647             0                 0            0                1   \n",
              "\n",
              "      Retailer_Foot Locker  Retailer_Kohl's  ...  Retailer_West Gear  \\\n",
              "0                        1                0  ...                   0   \n",
              "1                        1                0  ...                   0   \n",
              "2                        0                0  ...                   1   \n",
              "3                        1                0  ...                   0   \n",
              "4                        1                0  ...                   0   \n",
              "...                    ...              ...  ...                 ...   \n",
              "9643                     0                1  ...                   0   \n",
              "9644                     1                0  ...                   0   \n",
              "9645                     0                0  ...                   1   \n",
              "9646                     0                0  ...                   0   \n",
              "9647                     0                0  ...                   0   \n",
              "\n",
              "      Sales Method_In-store  Sales Method_Online  Sales Method_Outlet  \\\n",
              "0                         1                    0                    0   \n",
              "1                         0                    1                    0   \n",
              "2                         0                    1                    0   \n",
              "3                         0                    1                    0   \n",
              "4                         0                    1                    0   \n",
              "...                     ...                  ...                  ...   \n",
              "9643                      0                    1                    0   \n",
              "9644                      0                    1                    0   \n",
              "9645                      0                    1                    0   \n",
              "9646                      0                    1                    0   \n",
              "9647                      0                    1                    0   \n",
              "\n",
              "      Product_Men's Apparel  Product_Men's Athletic Footwear  \\\n",
              "0                         0                                0   \n",
              "1                         0                                0   \n",
              "2                         0                                0   \n",
              "3                         1                                0   \n",
              "4                         0                                0   \n",
              "...                     ...                              ...   \n",
              "9643                      1                                0   \n",
              "9644                      1                                0   \n",
              "9645                      0                                1   \n",
              "9646                      0                                0   \n",
              "9647                      0                                0   \n",
              "\n",
              "      Product_Men's Street Footwear  Product_Women's Apparel  \\\n",
              "0                                 1                        0   \n",
              "1                                 1                        0   \n",
              "2                                 0                        0   \n",
              "3                                 0                        0   \n",
              "4                                 0                        0   \n",
              "...                             ...                      ...   \n",
              "9643                              0                        0   \n",
              "9644                              0                        0   \n",
              "9645                              0                        0   \n",
              "9646                              0                        0   \n",
              "9647                              0                        0   \n",
              "\n",
              "      Product_Women's Athletic Footwear  Product_Women's Street Footwear  \n",
              "0                                     0                                0  \n",
              "1                                     0                                0  \n",
              "2                                     0                                1  \n",
              "3                                     0                                0  \n",
              "4                                     0                                1  \n",
              "...                                 ...                              ...  \n",
              "9643                                  0                                0  \n",
              "9644                                  0                                0  \n",
              "9645                                  0                                0  \n",
              "9646                                  1                                0  \n",
              "9647                                  0                                1  \n",
              "\n",
              "[9648 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3d24d1b5-3582-48f4-94b8-6cf203b723b1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price per Unit</th>\n",
              "      <th>Total Sales</th>\n",
              "      <th>Region_Midwest</th>\n",
              "      <th>Region_Northeast</th>\n",
              "      <th>Region_South</th>\n",
              "      <th>Region_Southeast</th>\n",
              "      <th>Region_West</th>\n",
              "      <th>Retailer_Amazon</th>\n",
              "      <th>Retailer_Foot Locker</th>\n",
              "      <th>Retailer_Kohl's</th>\n",
              "      <th>...</th>\n",
              "      <th>Retailer_West Gear</th>\n",
              "      <th>Sales Method_In-store</th>\n",
              "      <th>Sales Method_Online</th>\n",
              "      <th>Sales Method_Outlet</th>\n",
              "      <th>Product_Men's Apparel</th>\n",
              "      <th>Product_Men's Athletic Footwear</th>\n",
              "      <th>Product_Men's Street Footwear</th>\n",
              "      <th>Product_Women's Apparel</th>\n",
              "      <th>Product_Women's Athletic Footwear</th>\n",
              "      <th>Product_Women's Street Footwear</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35.0</td>\n",
              "      <td>21700.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25.0</td>\n",
              "      <td>18000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>61.0</td>\n",
              "      <td>7320.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>43.0</td>\n",
              "      <td>1505.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55.0</td>\n",
              "      <td>45375.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9643</th>\n",
              "      <td>32.0</td>\n",
              "      <td>2176.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9644</th>\n",
              "      <td>37.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9645</th>\n",
              "      <td>46.0</td>\n",
              "      <td>5750.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9646</th>\n",
              "      <td>42.0</td>\n",
              "      <td>8820.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9647</th>\n",
              "      <td>34.0</td>\n",
              "      <td>2618.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9648 rows × 22 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3d24d1b5-3582-48f4-94b8-6cf203b723b1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3d24d1b5-3582-48f4-94b8-6cf203b723b1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3d24d1b5-3582-48f4-94b8-6cf203b723b1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-406107fd-5de2-45ed-a8e9-371206c875bb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-406107fd-5de2-45ed-a8e9-371206c875bb')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-406107fd-5de2-45ed-a8e9-371206c875bb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df2"
            }
          },
          "metadata": {},
          "execution_count": 396
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "5f9a7cfa",
        "execution_start": 1708811825314,
        "execution_millis": 155,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "b4305d2fb6754f1da82d527361c6d7ed",
        "deepnote_cell_type": "code",
        "id": "SeMJ3TVSId66"
      },
      "source": [
        "Xlin = df2.values[:,(0,2,3,4,5,6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)]\n",
        "Ylin = df2.values[:, 1]\n",
        "\n",
        "\n",
        "Xlin_train, Xlin_test, ylin_train, ylin_test = train_test_split(Xlin, Ylin, test_size=0.1, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "c99adeaf",
        "execution_start": 1708811825330,
        "execution_millis": 139,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "ff574a90aa1544cb8dc42e901d8f1562",
        "deepnote_cell_type": "code",
        "id": "yX30mErEId66"
      },
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(Xlin_train,ylin_train)\n",
        "ylin_pred = lr.predict(Xlin_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "fac3f800",
        "execution_start": 1708811825451,
        "execution_millis": 218,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "b87eae0c1cf0424b8fc30ca7997054b0",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8fppRLyId66",
        "outputId": "8b762333-54e6-40e2-c65c-45e0ecb057ee"
      },
      "source": [
        "\n",
        "# Рассчет MSE\n",
        "mse = np.mean((ylin_test - ylin_pred)**2)\n",
        "\n",
        "# Рассчет MAPE\n",
        "mape = np.mean(np.abs((ylin_test - ylin_pred) / ylin_test)) * 100\n",
        "\n",
        "print(\"MSE:\", mse)\n",
        "print(\"MAPE:\", mape)\n",
        "#89.2313454435439"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 78744173.18875876\n",
            "MAPE: 94.23262361281427\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "47d9fc58",
        "execution_start": 1708811825459,
        "execution_millis": 514,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "d986e62a3cf24868bbf5b28275eb9367",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "BVW7LfTuId67",
        "outputId": "b193197a-bf1d-4751-b7c1-391423f04549"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.lines as mlines\n",
        "import matplotlib.transforms as mtransforms\n",
        "\n",
        "x, y = np.random.random((2, 100))*2\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(ylin_pred, ylin_test)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x1700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "8e7f49389a3a4e4298a415189b9e1eb4",
        "deepnote_cell_type": "text-cell-h1",
        "id": "Z2e3EcfOId67"
      },
      "source": [
        "# Cat BOOST на ненормализированных данных"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "1cd79bfb",
        "execution_start": 1708811825984,
        "execution_millis": 241,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "abae951ae21f4868bd5900bc84c78620",
        "deepnote_cell_type": "code",
        "id": "clZtrIYAId67"
      },
      "source": [
        "Xcat = df2.values[:,(0,2,3,4,5,6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)]\n",
        "Ycat = df2.values[:, 1]\n",
        "\n",
        "\n",
        "Xcat_train, Xcat_test, ycat_train, ycat_test = train_test_split(Xcat, Ycat, test_size=0.1, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "fa6b2910",
        "execution_start": 1708811825994,
        "execution_millis": 9357,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "667585eca92d45aea190196523d04855",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rY7LYXTUId67",
        "outputId": "c8f1dac2-7508-4435-e516-5bb203a1dfcb"
      },
      "source": [
        "from catboost import Pool, CatBoostRegressor\n",
        "\n",
        "# initialize Pool\n",
        "train_pool = Pool(Xcat_train, ycat_train)\n",
        "test_pool = Pool(Xcat_test, ycat_test)\n",
        "\n",
        "# specify the training parameters\n",
        "model = CatBoostRegressor(iterations=2500, loss_function='MAE', verbose=100)\n",
        "#train the model\n",
        "model.fit(train_pool)\n",
        "# make the prediction using the resulting model\n",
        "predscat = model.predict(test_pool)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0:\tlearn: 8198.6999242\ttotal: 5.86ms\tremaining: 14.6s\n",
            "100:\tlearn: 4975.2823528\ttotal: 434ms\tremaining: 10.3s\n",
            "200:\tlearn: 4561.4266573\ttotal: 753ms\tremaining: 8.61s\n",
            "300:\tlearn: 4316.5043043\ttotal: 1.1s\tremaining: 8.06s\n",
            "400:\tlearn: 4083.0619693\ttotal: 1.45s\tremaining: 7.6s\n",
            "500:\tlearn: 3886.6460835\ttotal: 1.64s\tremaining: 6.54s\n",
            "600:\tlearn: 3760.0635485\ttotal: 1.83s\tremaining: 5.77s\n",
            "700:\tlearn: 3660.5084792\ttotal: 2.01s\tremaining: 5.15s\n",
            "800:\tlearn: 3574.8484129\ttotal: 2.19s\tremaining: 4.66s\n",
            "900:\tlearn: 3495.8339108\ttotal: 2.39s\tremaining: 4.24s\n",
            "1000:\tlearn: 3434.5839859\ttotal: 2.58s\tremaining: 3.86s\n",
            "1100:\tlearn: 3383.4654947\ttotal: 2.77s\tremaining: 3.52s\n",
            "1200:\tlearn: 3336.9683849\ttotal: 2.96s\tremaining: 3.2s\n",
            "1300:\tlearn: 3286.6360927\ttotal: 3.14s\tremaining: 2.89s\n",
            "1400:\tlearn: 3250.3488043\ttotal: 3.33s\tremaining: 2.62s\n",
            "1500:\tlearn: 3214.4978168\ttotal: 3.51s\tremaining: 2.34s\n",
            "1600:\tlearn: 3177.0967655\ttotal: 3.69s\tremaining: 2.08s\n",
            "1700:\tlearn: 3151.4138832\ttotal: 3.88s\tremaining: 1.82s\n",
            "1800:\tlearn: 3123.8816358\ttotal: 4.06s\tremaining: 1.57s\n",
            "1900:\tlearn: 3097.1388331\ttotal: 4.24s\tremaining: 1.33s\n",
            "2000:\tlearn: 3074.5133780\ttotal: 4.44s\tremaining: 1.11s\n",
            "2100:\tlearn: 3054.2708146\ttotal: 4.62s\tremaining: 877ms\n",
            "2200:\tlearn: 3036.0216952\ttotal: 4.8s\tremaining: 652ms\n",
            "2300:\tlearn: 3020.2682115\ttotal: 4.97s\tremaining: 430ms\n",
            "2400:\tlearn: 3005.3054404\ttotal: 5.17s\tremaining: 213ms\n",
            "2499:\tlearn: 2991.6212552\ttotal: 5.35s\tremaining: 0us\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "5d8a9dc0",
        "execution_start": 1708811835357,
        "execution_millis": 111,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "76fecb4308f24b348abdf3b405c923ea",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ku5DpbhdId67",
        "outputId": "d9ea7c7d-3810-4789-bd3c-3c574beb1e5d"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "\n",
        "rmse = mean_squared_error(ycat_test, predscat, squared=False)\n",
        "mae = mean_absolute_error(ycat_test, predscat)\n",
        "# Рассчет MAPE\n",
        "mape = np.mean(np.abs((ycat_test - predscat) / ycat_test)) * 100\n",
        "\n",
        "print(\"MAE:\", mae)\n",
        "print(\"MAPE:\", mape)\n",
        "print(\"RMSE:\", rmse)\n",
        "#36.75463751878325\n",
        "#33.75402423022601\n",
        "#36.615710155370266"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 3527.9628741475603\n",
            "MAPE: 35.61015758819531\n",
            "RMSE: 6363.709139537316\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "6178cdce",
        "execution_start": 1708811835369,
        "execution_millis": 605,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "092bd3df80404036a2b7b37280a3ad20",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "AWPFfyK6Id67",
        "outputId": "4fce4921-9dfc-4a70-e636-989ef46c188e"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.lines as mlines\n",
        "import matplotlib.transforms as mtransforms\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(predscat,ycat_test)\n",
        "line = mlines.Line2D([0, 1], [0, 1], color='red')\n",
        "transform = ax.transAxes\n",
        "line.set_transform(transform)\n",
        "ax.add_line(line)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x1700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "ce87351b",
        "execution_start": 1708811835985,
        "execution_millis": 113,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "7a9cabe45aad431aaab62fc43c9be9b8",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hadv9P-TId67",
        "outputId": "7fb39e20-4a55-4ae2-8a5c-26d013c8bbd1"
      },
      "source": [
        "def calculate_bias(pred, truev):\n",
        "    # Вычисляем смещение (BIAS) данных относительно среднего значения\n",
        "    bias = sum(pred - truev) / len(truev)\n",
        "    return bias\n",
        "\n",
        "# Пример использования функции\n",
        "bias = calculate_bias(predscat, ycat_test)\n",
        "print(\"BIAS данных:\", bias)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BIAS данных: -548.4363052028035\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "9d1caca30e36410799acf5338f7e1d9d",
        "deepnote_cell_type": "text-cell-h1",
        "id": "-OlVkWvkId68"
      },
      "source": [
        "# XG BOOST для НЕнормализированных данных"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "1a7ad5a",
        "execution_start": 1708811840020,
        "execution_millis": 67,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "5ce0ede264d34465a47d8af5b249fff0",
        "deepnote_cell_type": "code",
        "id": "TD6Ty1q6Id68"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import accuracy_score\n",
        "from xgboost import XGBRegressor\n",
        "import plotly.express as px"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "43dbfba5",
        "execution_start": 1708811840049,
        "execution_millis": 48,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "2337ca26d29f4c5f8ad49be8fbae6306",
        "deepnote_cell_type": "code",
        "id": "u8_8vsuiId68"
      },
      "source": [
        "Xgb = df2.values[:,(0,2,3,4,5,6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)]\n",
        "Ygb = df2.values[:, 1]\n",
        "\n",
        "\n",
        "Xgb_train, Xgb_test, ygb_train, ygb_test = train_test_split(Xgb, Ygb, test_size=0.1, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "ce91da8d",
        "execution_start": 1708811840059,
        "execution_millis": 1196,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "9af6cb68aa2b4dbda5206ea70dc114d7",
        "deepnote_cell_type": "code",
        "id": "Hh2AdJ4VId68"
      },
      "source": [
        "bst = XGBRegressor(n_estimators=100, max_depth=16, objective='reg:squarederror',eval_metric=\"rmse\")\n",
        "\n",
        "# fit model\n",
        "bst.fit(Xgb_train, ygb_train)\n",
        "\n",
        "predsgb = bst.predict(Xgb_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "45a0e081",
        "execution_start": 1708811841257,
        "execution_millis": 78,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "f17a197bec0b4d0e93e56d7ea1ead3d2",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1CNPLFbnId68",
        "outputId": "94f63396-adae-4ec5-9d7d-fa33fbc53174"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "\n",
        "rmse = mean_squared_error(ygb_test, predsgb, squared=False)\n",
        "mae = mean_absolute_error(ygb_test, predsgb)\n",
        "# Рассчет MAPE\n",
        "mape = np.mean(np.abs((ygb_test - predsgb) / ygb_test)) * 100\n",
        "\n",
        "print(\"MAE:\", mae)\n",
        "print(\"MAPE:\", mape)\n",
        "print(\"RMSE:\", rmse)\n",
        "#45.1036560331101\n",
        "#39.75442084840644\n",
        "#39.989281025981214"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 3677.7674905569443\n",
            "MAPE: 44.09313470272754\n",
            "RMSE: 6685.080666416931\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "3e618d9a",
        "execution_start": 1708811841283,
        "execution_millis": 395,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "5f0c7365134b4e2bb0cf21f0bc7fa1d8",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "5GfpynIYId68",
        "outputId": "71d491be-38a5-4dd0-eee3-f942d2f31971"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.lines as mlines\n",
        "import matplotlib.transforms as mtransforms\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(predsgb, ygb_test)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x1700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "fb8ea564f8d94f8da38296331a21be41",
        "deepnote_cell_type": "text-cell-h1",
        "id": "qtawYbkaId69"
      },
      "source": [
        "# Данные с нормализацией"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "2319ffb05dc543aba1aff5227e109197",
        "deepnote_cell_type": "text-cell-p",
        "id": "eaqZXnsjId69"
      },
      "source": [
        "Данные с нормализацией"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "8628eef3",
        "execution_start": 1708811841693,
        "execution_millis": 305,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "30f791fbd5db4c36a486265e4c447ccc",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "JvLAfpBlId69",
        "outputId": "fad4e6b8-ea77-4b97-c80b-741f99f390c6"
      },
      "source": [
        "dfn = df2.copy()\n",
        "dfn[\"Total Sales\"] = 1000 * ((dfn[\"Total Sales\"] - dfn[\"Total Sales\"].min()) / (dfn[\"Total Sales\"].max() - dfn[\"Total Sales\"].min()))\n",
        "dfn"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Price per Unit  Total Sales  Region_Midwest  Region_Northeast  \\\n",
              "0               35.0   263.030303               1                 0   \n",
              "1               25.0   218.181818               0                 0   \n",
              "2               61.0    88.727273               0                 0   \n",
              "3               43.0    18.242424               1                 0   \n",
              "4               55.0   550.000000               0                 0   \n",
              "...              ...          ...             ...               ...   \n",
              "9643            32.0    26.375758               1                 0   \n",
              "9644            37.0     3.139394               1                 0   \n",
              "9645            46.0    69.696970               0                 0   \n",
              "9646            42.0   106.909091               0                 0   \n",
              "9647            34.0    31.733333               0                 1   \n",
              "\n",
              "      Region_South  Region_Southeast  Region_West  Retailer_Amazon  \\\n",
              "0                0                 0            0                0   \n",
              "1                1                 0            0                0   \n",
              "2                0                 0            1                0   \n",
              "3                0                 0            0                0   \n",
              "4                0                 1            0                0   \n",
              "...            ...               ...          ...              ...   \n",
              "9643             0                 0            0                0   \n",
              "9644             0                 0            0                0   \n",
              "9645             1                 0            0                0   \n",
              "9646             0                 1            0                0   \n",
              "9647             0                 0            0                1   \n",
              "\n",
              "      Retailer_Foot Locker  Retailer_Kohl's  ...  Retailer_West Gear  \\\n",
              "0                        1                0  ...                   0   \n",
              "1                        1                0  ...                   0   \n",
              "2                        0                0  ...                   1   \n",
              "3                        1                0  ...                   0   \n",
              "4                        1                0  ...                   0   \n",
              "...                    ...              ...  ...                 ...   \n",
              "9643                     0                1  ...                   0   \n",
              "9644                     1                0  ...                   0   \n",
              "9645                     0                0  ...                   1   \n",
              "9646                     0                0  ...                   0   \n",
              "9647                     0                0  ...                   0   \n",
              "\n",
              "      Sales Method_In-store  Sales Method_Online  Sales Method_Outlet  \\\n",
              "0                         1                    0                    0   \n",
              "1                         0                    1                    0   \n",
              "2                         0                    1                    0   \n",
              "3                         0                    1                    0   \n",
              "4                         0                    1                    0   \n",
              "...                     ...                  ...                  ...   \n",
              "9643                      0                    1                    0   \n",
              "9644                      0                    1                    0   \n",
              "9645                      0                    1                    0   \n",
              "9646                      0                    1                    0   \n",
              "9647                      0                    1                    0   \n",
              "\n",
              "      Product_Men's Apparel  Product_Men's Athletic Footwear  \\\n",
              "0                         0                                0   \n",
              "1                         0                                0   \n",
              "2                         0                                0   \n",
              "3                         1                                0   \n",
              "4                         0                                0   \n",
              "...                     ...                              ...   \n",
              "9643                      1                                0   \n",
              "9644                      1                                0   \n",
              "9645                      0                                1   \n",
              "9646                      0                                0   \n",
              "9647                      0                                0   \n",
              "\n",
              "      Product_Men's Street Footwear  Product_Women's Apparel  \\\n",
              "0                                 1                        0   \n",
              "1                                 1                        0   \n",
              "2                                 0                        0   \n",
              "3                                 0                        0   \n",
              "4                                 0                        0   \n",
              "...                             ...                      ...   \n",
              "9643                              0                        0   \n",
              "9644                              0                        0   \n",
              "9645                              0                        0   \n",
              "9646                              0                        0   \n",
              "9647                              0                        0   \n",
              "\n",
              "      Product_Women's Athletic Footwear  Product_Women's Street Footwear  \n",
              "0                                     0                                0  \n",
              "1                                     0                                0  \n",
              "2                                     0                                1  \n",
              "3                                     0                                0  \n",
              "4                                     0                                1  \n",
              "...                                 ...                              ...  \n",
              "9643                                  0                                0  \n",
              "9644                                  0                                0  \n",
              "9645                                  0                                0  \n",
              "9646                                  1                                0  \n",
              "9647                                  0                                1  \n",
              "\n",
              "[9648 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-809b9f58-1433-43f4-bd38-785a6c8af20b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price per Unit</th>\n",
              "      <th>Total Sales</th>\n",
              "      <th>Region_Midwest</th>\n",
              "      <th>Region_Northeast</th>\n",
              "      <th>Region_South</th>\n",
              "      <th>Region_Southeast</th>\n",
              "      <th>Region_West</th>\n",
              "      <th>Retailer_Amazon</th>\n",
              "      <th>Retailer_Foot Locker</th>\n",
              "      <th>Retailer_Kohl's</th>\n",
              "      <th>...</th>\n",
              "      <th>Retailer_West Gear</th>\n",
              "      <th>Sales Method_In-store</th>\n",
              "      <th>Sales Method_Online</th>\n",
              "      <th>Sales Method_Outlet</th>\n",
              "      <th>Product_Men's Apparel</th>\n",
              "      <th>Product_Men's Athletic Footwear</th>\n",
              "      <th>Product_Men's Street Footwear</th>\n",
              "      <th>Product_Women's Apparel</th>\n",
              "      <th>Product_Women's Athletic Footwear</th>\n",
              "      <th>Product_Women's Street Footwear</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35.0</td>\n",
              "      <td>263.030303</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25.0</td>\n",
              "      <td>218.181818</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>61.0</td>\n",
              "      <td>88.727273</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>43.0</td>\n",
              "      <td>18.242424</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55.0</td>\n",
              "      <td>550.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9643</th>\n",
              "      <td>32.0</td>\n",
              "      <td>26.375758</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9644</th>\n",
              "      <td>37.0</td>\n",
              "      <td>3.139394</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9645</th>\n",
              "      <td>46.0</td>\n",
              "      <td>69.696970</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9646</th>\n",
              "      <td>42.0</td>\n",
              "      <td>106.909091</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9647</th>\n",
              "      <td>34.0</td>\n",
              "      <td>31.733333</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9648 rows × 22 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-809b9f58-1433-43f4-bd38-785a6c8af20b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-809b9f58-1433-43f4-bd38-785a6c8af20b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-809b9f58-1433-43f4-bd38-785a6c8af20b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-64cc7b50-2d21-411b-9822-7e26877f7353\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-64cc7b50-2d21-411b-9822-7e26877f7353')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-64cc7b50-2d21-411b-9822-7e26877f7353 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dfn"
            }
          },
          "metadata": {},
          "execution_count": 411
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "7db4c1b8",
        "execution_start": 1708811841775,
        "execution_millis": 224,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "f85acee6c39e4df298c0357b56f9af9e",
        "deepnote_cell_type": "code",
        "id": "JJjIld1lId69"
      },
      "source": [
        "Xn= dfn.values[:,(0,2,3,4,5,6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)]\n",
        "Yn= dfn.values[:, 1]\n",
        "\n",
        "\n",
        "Xn_train, Xn_test, yn_train, yn_test = train_test_split(Xn, Yn, test_size=0.15, random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "e0d524fdf13149158a9aee36e2ca02ef",
        "deepnote_cell_type": "text-cell-h1",
        "id": "uzfLhKOYId69"
      },
      "source": [
        "# CATBOOST для нормализированных данных"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "15a132d8",
        "execution_start": 1708811841907,
        "execution_millis": 7174,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "234b02bd6aef40e8952851c813dec070",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wDr64pCSId69",
        "outputId": "78465634-b546-4a12-815f-1f55832f0dc4"
      },
      "source": [
        "from catboost import Pool, CatBoostRegressor\n",
        "\n",
        "# initialize Pool\n",
        "train_pool = Pool(Xn_train, yn_train)\n",
        "test_pool = Pool(Xn_test, yn_test)\n",
        "\n",
        "# specify the training parameters\n",
        "model = CatBoostRegressor(iterations=2500, loss_function='MAE', verbose=100)\n",
        "#train the model\n",
        "model.fit(train_pool)\n",
        "# make the prediction using the resulting model\n",
        "predsn = model.predict(test_pool)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0:\tlearn: 100.1006295\ttotal: 2.03ms\tremaining: 5.06s\n",
            "100:\tlearn: 60.5652253\ttotal: 181ms\tremaining: 4.3s\n",
            "200:\tlearn: 55.2807466\ttotal: 369ms\tremaining: 4.21s\n",
            "300:\tlearn: 52.2903259\ttotal: 872ms\tremaining: 6.37s\n",
            "400:\tlearn: 49.4570852\ttotal: 1.41s\tremaining: 7.37s\n",
            "500:\tlearn: 47.2914255\ttotal: 1.79s\tremaining: 7.14s\n",
            "600:\tlearn: 45.7184235\ttotal: 2.36s\tremaining: 7.46s\n",
            "700:\tlearn: 44.3811195\ttotal: 3s\tremaining: 7.71s\n",
            "800:\tlearn: 43.3340754\ttotal: 3.66s\tremaining: 7.76s\n",
            "900:\tlearn: 42.4497246\ttotal: 4.13s\tremaining: 7.33s\n",
            "1000:\tlearn: 41.5706125\ttotal: 4.55s\tremaining: 6.82s\n",
            "1100:\tlearn: 40.9200571\ttotal: 4.87s\tremaining: 6.19s\n",
            "1200:\tlearn: 40.3708659\ttotal: 5.3s\tremaining: 5.73s\n",
            "1300:\tlearn: 39.8448108\ttotal: 5.7s\tremaining: 5.25s\n",
            "1400:\tlearn: 39.4449101\ttotal: 6.15s\tremaining: 4.83s\n",
            "1500:\tlearn: 39.1004153\ttotal: 6.75s\tremaining: 4.49s\n",
            "1600:\tlearn: 38.6542085\ttotal: 7.59s\tremaining: 4.26s\n",
            "1700:\tlearn: 38.3007221\ttotal: 8.34s\tremaining: 3.92s\n",
            "1800:\tlearn: 37.9591113\ttotal: 9.12s\tremaining: 3.54s\n",
            "1900:\tlearn: 37.6722441\ttotal: 9.99s\tremaining: 3.15s\n",
            "2000:\tlearn: 37.3940773\ttotal: 10.6s\tremaining: 2.65s\n",
            "2100:\tlearn: 37.1837047\ttotal: 10.8s\tremaining: 2.05s\n",
            "2200:\tlearn: 36.9606475\ttotal: 11s\tremaining: 1.49s\n",
            "2300:\tlearn: 36.7552919\ttotal: 11.1s\tremaining: 962ms\n",
            "2400:\tlearn: 36.5775629\ttotal: 11.3s\tremaining: 466ms\n",
            "2499:\tlearn: 36.4027652\ttotal: 11.5s\tremaining: 0us\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "e818c26f",
        "execution_start": 1708811849109,
        "execution_millis": 457,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "0bcd77b6a11940c280aa7acfea3b0bcc",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        },
        "id": "SGkdI4UPId69",
        "outputId": "0b0a4569-b9d8-452e-8119-353bb00ff1d6"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.lines as mlines\n",
        "import matplotlib.transforms as mtransforms\n",
        "\n",
        "x, y = np.random.random((2, 100))*2\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(predsn, yn_test)\n",
        "line = mlines.Line2D([0, 1], [0, 1], color='red')\n",
        "transform = ax.transAxes\n",
        "line.set_transform(transform)\n",
        "ax.add_line(line)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x1700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "2cfdec28",
        "execution_start": 1708811849567,
        "execution_millis": 334,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "b0cdef4732a3413e9f4283ff7c7c47c9",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8U61x8bBId6-",
        "outputId": "04ff7dbc-43c7-4810-9c5a-5f5e79f34423"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "\n",
        "rmse = mean_squared_error(yn_test, predsn, squared=False)\n",
        "mae = mean_absolute_error(yn_test, predsn)\n",
        "# Рассчет MAPE\n",
        "mape = np.mean((np.abs(yn_test - predsn)) / (yn_test)) * 100\n",
        "\n",
        "print(\"MAE:\", mae)\n",
        "print(\"MAPE:\", mape)\n",
        "print(\"RMSE:\", rmse)\n",
        "#35.109849965726916\n",
        "#36.17786197587769\n",
        "#35.84043296502193"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 88.78766978094761\n",
            "MAPE: 49.211447473543785\n",
            "RMSE: 159.41655281287044\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "b6b2987d",
        "execution_start": 1708811849567,
        "execution_millis": 334,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "dc9e5f5eaed44ccba42a45dab3a44835",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2n3LPBQId6-",
        "outputId": "9a44a1c0-eeb4-4631-f471-22d1d4a03e00"
      },
      "source": [
        "bias = calculate_bias(predsn, yn_test)\n",
        "print(\"BIAS данных:\", bias)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BIAS данных: -8.382038883018813\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "65deb81030b94cdd9905668c1422c568",
        "deepnote_cell_type": "text-cell-h1",
        "id": "cB_dpM8AId6-"
      },
      "source": [
        "# Линейная Регрессия Для нормализированных данных"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "d3bf7e87",
        "execution_start": 1708811849568,
        "execution_millis": 333,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "dfe36a2ab8344b08acf6e00ab61164b8",
        "deepnote_cell_type": "code",
        "id": "pbp7_jLxId6-"
      },
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(Xn_train,yn_train)\n",
        "y_pred = lr.predict(Xn_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "cbf1a6a8",
        "execution_start": 1708811849656,
        "execution_millis": 245,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "0d9540ada12243a5b9cb801c495fd722",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufHODl4XId6-",
        "outputId": "daf4a020-e63c-4926-8636-6c8a253e7a42"
      },
      "source": [
        "# Рассчет MSE\n",
        "mse = np.mean((yn_test - y_pred)**2)\n",
        "\n",
        "# Рассчет MAPE\n",
        "mape = np.mean(np.abs((yn_test - y_pred)) / (yn_test)) * 100\n",
        "\n",
        "print(\"MSE:\", mse)\n",
        "print(\"MAPE:\", mape)\n",
        "#87.54144278462634\n",
        "#98.21869232813982\n",
        "#89.93882815686327"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 12063.981433798888\n",
            "MAPE: 97.37533213641754\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "52da4382",
        "execution_start": 1708811849673,
        "execution_millis": 580,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "219b65f12b3a4df999bf1f1272015747",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        },
        "id": "-jeMHtD5Id6-",
        "outputId": "7bc207c2-f73f-4fa8-ea5d-df13b2b8c0a2"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.lines as mlines\n",
        "import matplotlib.transforms as mtransforms\n",
        "\n",
        "x, y = np.random.random((2, 100))*2\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(y_pred, yn_test)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x1700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "9c3b708b99444d00a2f86db22c7750f6",
        "deepnote_cell_type": "text-cell-h1",
        "id": "In4oVk0XId6-"
      },
      "source": [
        "# XGBOOST для нормализированных данных"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "1a7ad5a",
        "execution_start": 1708811850257,
        "execution_millis": 121,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "74245d5b9be34120843a8a2d8ef7735f",
        "deepnote_cell_type": "code",
        "id": "zM8nnpe-Id6-"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import accuracy_score\n",
        "from xgboost import XGBRegressor\n",
        "import plotly.express as px"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "146f9c60",
        "execution_start": 1708811850272,
        "execution_millis": 1284,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "4a4a1b7a88d74b14ab7a612d64ba4598",
        "deepnote_cell_type": "code",
        "id": "uYxyqox_Id6-"
      },
      "source": [
        "bst = XGBRegressor(n_estimators=100, max_depth=16, objective='reg:squarederror',eval_metric=\"rmse\")\n",
        "\n",
        "# fit model\n",
        "bst.fit(Xn_train, yn_train)\n",
        "\n",
        "predsgb = bst.predict(Xn_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "2698df17",
        "execution_start": 1708811851563,
        "execution_millis": 22,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "09dd2a87f539478a80db22b27652c18f",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t7r7yR-QId6_",
        "outputId": "92df348c-d25f-4795-9e78-ff957d8c3b77"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "\n",
        "rmse = mean_squared_error(yn_test, predsgb, squared=False)\n",
        "mae = mean_absolute_error(yn_test, predsgb)\n",
        "# Рассчет MAPE\n",
        "mape = np.mean((np.abs((yn_test - predsgb) / yn_test))) * 100\n",
        "\n",
        "print(\"MAE:\", mae)\n",
        "print(\"MAPE:\", mape)\n",
        "print(\"RMSE:\", rmse)\n",
        "#39.87636376786854\n",
        "#44.37471825067739"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 39.13227015021217\n",
            "MAPE: 38.88523414216572\n",
            "RMSE: 68.57292485957204\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "dca3ca74",
        "execution_start": 1708811851575,
        "execution_millis": 484,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "2e1b68c6243b4b5193101853baa7d85f",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        },
        "id": "tCQASnvMId6_",
        "outputId": "81bce3e3-6bf0-4680-947f-7aa2f1a06d20"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.lines as mlines\n",
        "import matplotlib.transforms as mtransforms\n",
        "\n",
        "x, y = np.random.random((2, 100))*2\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(predsgb, yn_test)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x1700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "d5273e48eb6647e2a00329b7d8c4081f",
        "deepnote_cell_type": "text-cell-h1",
        "id": "_ufKnaWuId6_"
      },
      "source": [
        "# Данные без \"хвоста\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "814fa477",
        "execution_start": 1708811852069,
        "execution_millis": 283,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "348e4aacc8594fe4a6c5248fc8c9f0e7",
        "deepnote_cell_type": "code",
        "id": "HugNdGm9Id6_"
      },
      "source": [
        "halfdata = df2.copy()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "34b429e9",
        "execution_start": 1708811852079,
        "execution_millis": 273,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "2ee94f7e6b1341efbebf8cf08339f46f",
        "deepnote_cell_type": "code",
        "id": "uGFo7qy6Id6_"
      },
      "source": [
        "halfdata[\"Sales\"] = halfdata[\"Total Sales\"]\n",
        "halfdata = halfdata.drop(\"Total Sales\", axis=1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "4c672484",
        "execution_start": 1708811852095,
        "execution_millis": 257,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "0bf34ac712914bcdb4e27f55cfdc93ae",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "IdN06ru3Id6_",
        "outputId": "d18aa0b0-e196-447f-da86-8d25426c9afe"
      },
      "source": [
        "halfdata = halfdata[halfdata.Sales < 50000]\n",
        "halfdata"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Price per Unit  Region_Midwest  Region_Northeast  Region_South  \\\n",
              "0               35.0               1                 0             0   \n",
              "1               25.0               0                 0             1   \n",
              "2               61.0               0                 0             0   \n",
              "3               43.0               1                 0             0   \n",
              "4               55.0               0                 0             0   \n",
              "...              ...             ...               ...           ...   \n",
              "9643            32.0               1                 0             0   \n",
              "9644            37.0               1                 0             0   \n",
              "9645            46.0               0                 0             1   \n",
              "9646            42.0               0                 0             0   \n",
              "9647            34.0               0                 1             0   \n",
              "\n",
              "      Region_Southeast  Region_West  Retailer_Amazon  Retailer_Foot Locker  \\\n",
              "0                    0            0                0                     1   \n",
              "1                    0            0                0                     1   \n",
              "2                    0            1                0                     0   \n",
              "3                    0            0                0                     1   \n",
              "4                    1            0                0                     1   \n",
              "...                ...          ...              ...                   ...   \n",
              "9643                 0            0                0                     0   \n",
              "9644                 0            0                0                     1   \n",
              "9645                 0            0                0                     0   \n",
              "9646                 1            0                0                     0   \n",
              "9647                 0            0                1                     0   \n",
              "\n",
              "      Retailer_Kohl's  Retailer_Sports Direct  ...  Sales Method_In-store  \\\n",
              "0                   0                       0  ...                      1   \n",
              "1                   0                       0  ...                      0   \n",
              "2                   0                       0  ...                      0   \n",
              "3                   0                       0  ...                      0   \n",
              "4                   0                       0  ...                      0   \n",
              "...               ...                     ...  ...                    ...   \n",
              "9643                1                       0  ...                      0   \n",
              "9644                0                       0  ...                      0   \n",
              "9645                0                       0  ...                      0   \n",
              "9646                0                       1  ...                      0   \n",
              "9647                0                       0  ...                      0   \n",
              "\n",
              "      Sales Method_Online  Sales Method_Outlet  Product_Men's Apparel  \\\n",
              "0                       0                    0                      0   \n",
              "1                       1                    0                      0   \n",
              "2                       1                    0                      0   \n",
              "3                       1                    0                      1   \n",
              "4                       1                    0                      0   \n",
              "...                   ...                  ...                    ...   \n",
              "9643                    1                    0                      1   \n",
              "9644                    1                    0                      1   \n",
              "9645                    1                    0                      0   \n",
              "9646                    1                    0                      0   \n",
              "9647                    1                    0                      0   \n",
              "\n",
              "      Product_Men's Athletic Footwear  Product_Men's Street Footwear  \\\n",
              "0                                   0                              1   \n",
              "1                                   0                              1   \n",
              "2                                   0                              0   \n",
              "3                                   0                              0   \n",
              "4                                   0                              0   \n",
              "...                               ...                            ...   \n",
              "9643                                0                              0   \n",
              "9644                                0                              0   \n",
              "9645                                1                              0   \n",
              "9646                                0                              0   \n",
              "9647                                0                              0   \n",
              "\n",
              "      Product_Women's Apparel  Product_Women's Athletic Footwear  \\\n",
              "0                           0                                  0   \n",
              "1                           0                                  0   \n",
              "2                           0                                  0   \n",
              "3                           0                                  0   \n",
              "4                           0                                  0   \n",
              "...                       ...                                ...   \n",
              "9643                        0                                  0   \n",
              "9644                        0                                  0   \n",
              "9645                        0                                  0   \n",
              "9646                        0                                  1   \n",
              "9647                        0                                  0   \n",
              "\n",
              "      Product_Women's Street Footwear    Sales  \n",
              "0                                   0  21700.0  \n",
              "1                                   0  18000.0  \n",
              "2                                   1   7320.0  \n",
              "3                                   0   1505.0  \n",
              "4                                   1  45375.0  \n",
              "...                               ...      ...  \n",
              "9643                                0   2176.0  \n",
              "9644                                0    259.0  \n",
              "9645                                0   5750.0  \n",
              "9646                                0   8820.0  \n",
              "9647                                1   2618.0  \n",
              "\n",
              "[9430 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ced9458-db6b-4d4c-8f03-2972df43abdb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price per Unit</th>\n",
              "      <th>Region_Midwest</th>\n",
              "      <th>Region_Northeast</th>\n",
              "      <th>Region_South</th>\n",
              "      <th>Region_Southeast</th>\n",
              "      <th>Region_West</th>\n",
              "      <th>Retailer_Amazon</th>\n",
              "      <th>Retailer_Foot Locker</th>\n",
              "      <th>Retailer_Kohl's</th>\n",
              "      <th>Retailer_Sports Direct</th>\n",
              "      <th>...</th>\n",
              "      <th>Sales Method_In-store</th>\n",
              "      <th>Sales Method_Online</th>\n",
              "      <th>Sales Method_Outlet</th>\n",
              "      <th>Product_Men's Apparel</th>\n",
              "      <th>Product_Men's Athletic Footwear</th>\n",
              "      <th>Product_Men's Street Footwear</th>\n",
              "      <th>Product_Women's Apparel</th>\n",
              "      <th>Product_Women's Athletic Footwear</th>\n",
              "      <th>Product_Women's Street Footwear</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7320.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>43.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1505.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>45375.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9643</th>\n",
              "      <td>32.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2176.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9644</th>\n",
              "      <td>37.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>259.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9645</th>\n",
              "      <td>46.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5750.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9646</th>\n",
              "      <td>42.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8820.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9647</th>\n",
              "      <td>34.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2618.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9430 rows × 22 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ced9458-db6b-4d4c-8f03-2972df43abdb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8ced9458-db6b-4d4c-8f03-2972df43abdb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8ced9458-db6b-4d4c-8f03-2972df43abdb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d8dd4711-9981-49fd-9169-87f6a4d15b70\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d8dd4711-9981-49fd-9169-87f6a4d15b70')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d8dd4711-9981-49fd-9169-87f6a4d15b70 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "halfdata"
            }
          },
          "metadata": {},
          "execution_count": 426
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "9b4479ea",
        "execution_start": 1708811852192,
        "execution_millis": 377,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "8cd0e296ca3e42f6aa30c480fc4f2a66",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "OaZb489yId6_",
        "outputId": "7350b15e-1813-4a0a-dd30-3b7ed381e416"
      },
      "source": [
        "import plotly.express as px\n",
        "fig = px.histogram(halfdata, x=\"Sales\", title=\"Distribution of sales\")\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"dac88c3e-1c78-4605-a622-e46bc82d78a8\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"dac88c3e-1c78-4605-a622-e46bc82d78a8\")) {                    Plotly.newPlot(                        \"dac88c3e-1c78-4605-a622-e46bc82d78a8\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"Sales=%{x}\\u003cbr\\u003ecount=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[21700.0,18000.0,7320.0,1505.0,45375.0,5096.0,43500.0,5586.0,18800.0,28875.0,23000.0,3375.0,4500.0,7875.0,2997.0,6084.0,1470.0,3600.0,2688.0,35625.0,32500.0,4680.0,3051.0,6930.0,10199.0,4060.0,23500.0,13500.0,12375.0,20000.0,9261.0,7029.0,22500.0,7950.0,25000.0,5940.0,5488.0,954.0,3080.0,43750.0,13250.0,8160.0,13464.0,725.0,20000.0,667.0,7000.0,11024.0,5625.0,2535.0,7344.0,3696.0,5712.0,1764.0,9548.0,22500.0,3948.0,8556.0,43875.0,7524.0,5244.0,3486.0,2736.0,44000.0,11988.0,6000.0,8120.0,41250.0,3025.0,1560.0,44000.0,3619.0,23625.0,1222.0,2958.0,7500.0,16500.0,23625.0,4879.0,5625.0,6000.0,7920.0,31875.0,6674.0,7038.0,5460.0,3612.0,2640.0,3060.0,37125.0,12348.0,4620.0,45000.0,25375.0,7828.0,3078.0,6232.0,17500.0,5151.0,2337.0,9682.0,16250.0,2592.0,1740.0,3744.0,22500.0,15352.0,4512.0,8820.0,2775.0,18750.0,4200.0,5250.0,5166.0,26000.0,27500.0,4165.0,2940.0,35625.0,2465.0,12000.0,6000.0,1952.0,13500.0,4356.0,5076.0,13923.0,1248.0,10704.0,13583.0,5184.0,16832.0,5625.0,5727.0,21875.0,15525.0,8322.0,5922.0,2679.0,11395.0,35750.0,972.0,3075.0,9454.0,3100.0,3450.0,7000.0,1500.0,6171.0,1444.0,1406.0,7500.0,34500.0,5076.0,6000.0,6623.0,49500.0,2880.0,49500.0,5012.0,5160.0,12500.0,8320.0,15000.0,999.0,6816.0,47500.0,24000.0,41000.0,8058.0,9432.0,5530.0,5000.0,6148.0,41250.0,1617.0,19250.0,20145.0,5166.0,12500.0,27000.0,43125.0,5775.0,6757.0,13981.0,11250.0,6318.0,7140.0,36000.0,2800.0,1081.0,26250.0,1938.0,2368.0,11025.0,2537.0,2145.0,4370.0,9500.0,8745.0,2268.0,39000.0,4056.0,8250.0,3904.0,10556.0,7875.0,15625.0,2464.0,8640.0,9408.0,3870.0,9452.0,2940.0,2250.0,32500.0,3168.0,16170.0,15750.0,9625.0,2938.0,29750.0,3450.0,12000.0,2656.0,3627.0,12375.0,3366.0,3000.0,11200.0,9000.0,10504.0,11000.0,2176.0,3403.0,3256.0,4046.0,1120.0,9430.0,7644.0,18750.0,23750.0,7995.0,9900.0,9625.0,21375.0,1368.0,7847.0,705.0,39375.0,6045.0,4902.0,6253.0,13500.0,20625.0,27250.0,5985.0,4508.0,2241.0,2756.0,6240.0,5332.0,8208.0,23750.0,19000.0,4284.0,7475.0,15750.0,11250.0,4352.0,12750.0,42250.0,2737.0,2040.0,540.0,5198.0,22000.0,3024.0,3330.0,2639.0,24375.0,12285.0,9660.0,12000.0,5632.0,3360.0,26250.0,2727.0,15912.0,7500.0,19250.0,4004.0,7943.0,4564.0,1292.0,42000.0,3510.0,12900.0,7975.0,11554.0,33750.0,14625.0,42750.0,3591.0,5824.0,14760.0,45000.0,4756.0,29250.0,26250.0,10450.0,28500.0,3422.0,18000.0,22500.0,11184.0,2530.0,4200.0,3828.0,38000.0,6076.0,1998.0,5684.0,3237.0,4230.0,2436.0,18000.0,3300.0,13300.0,10976.0,15750.0,17000.0,7544.0,9000.0,16500.0,7848.0,1620.0,5328.0,2156.0,4140.0,3213.0,10368.0,1189.0,2146.0,1848.0,49000.0,3696.0,21875.0,5474.0,8250.0,7425.0,7155.0,1870.0,42000.0,2016.0,2145.0,47500.0,5886.0,15008.0,11088.0,41625.0,3024.0,4403.0,10034.0,2714.0,12000.0,8736.0,3944.0,15750.0,4185.0,3332.0,3277.0,8512.0,20000.0,11250.0,7840.0,6150.0,21000.0,38500.0,6250.0,23000.0,966.0,28000.0,2021.0,11000.0,14875.0,3211.0,1792.0,4042.0,1827.0,33000.0,4732.0,28125.0,5376.0,16875.0,36000.0,16250.0,11000.0,2000.0,13104.0,26250.0,9625.0,21375.0,8642.0,30000.0,35000.0,10686.0,36000.0,6794.0,5040.0,21000.0,3869.0,12000.0,4032.0,16000.0,12788.0,3672.0,10070.0,5640.0,24000.0,27625.0,16000.0,5460.0,1495.0,1504.0,5000.0,2806.0,3164.0,3182.0,10125.0,13312.0,10742.0,1520.0,8648.0,9625.0,9758.0,5800.0,3690.0,5625.0,6000.0,15512.0,11480.0,24500.0,1232.0,9450.0,2408.0,3780.0,16625.0,12750.0,26125.0,7680.0,2430.0,18000.0,2983.0,7995.0,11000.0,21000.0,13500.0,615.0,10000.0,4095.0,4375.0,1968.0,43750.0,1734.0,10840.0,7003.0,6125.0,7917.0,5360.0,11850.0,5070.0,875.0,30875.0,3534.0,2720.0,8096.0,8000.0,3500.0,8000.0,2665.0,3332.0,24000.0,5633.0,33750.0,7500.0,14000.0,11935.0,17875.0,13125.0,3196.0,27500.0,2655.0,7500.0,4500.0,5625.0,19250.0,2704.0,15125.0,40000.0,28000.0,18375.0,2898.0,9775.0,37125.0,5236.0,2961.0,9880.0,13500.0,7400.0,3648.0,6392.0,7616.0,8686.0,3100.0,36250.0,35750.0,6265.0,22500.0,4800.0,16849.0,10500.0,1599.0,17500.0,2491.0,10000.0,38500.0,5625.0,11000.0,10000.0,19500.0,33000.0,4452.0,6545.0,9506.0,5355.0,6250.0,11584.0,7105.0,4750.0,16590.0,6834.0,14455.0,6401.0,7802.0,9581.0,6105.0,27125.0,5376.0,3000.0,9840.0,8750.0,5832.0,3774.0,6815.0,33000.0,4480.0,7500.0,2660.0,7875.0,32500.0,5100.0,38250.0,11375.0,1560.0,5882.0,7668.0,19250.0,4002.0,3430.0,7623.0,4845.0,27000.0,3995.0,13500.0,9000.0,9856.0,5535.0,4116.0,37375.0,2850.0,6270.0,9000.0,3488.0,4500.0,47250.0,4263.0,26250.0,28875.0,2925.0,10488.0,3384.0,6500.0,8073.0,1617.0,7000.0,33750.0,22500.0,3750.0,8750.0,22500.0,4687.0,9000.0,8925.0,10000.0,5148.0,7560.0,28125.0,10584.0,14875.0,5763.0,5000.0,2380.0,14094.0,3100.0,2806.0,38000.0,3333.0,6365.0,14625.0,2684.0,12784.0,4875.0,2106.0,20250.0,2332.0,6683.0,12992.0,3245.0,9558.0,4785.0,33000.0,21375.0,34125.0,12000.0,7434.0,17875.0,5184.0,45375.0,22000.0,1900.0,48125.0,6000.0,23625.0,21000.0,14000.0,15660.0,6875.0,7875.0,21375.0,6000.0,3825.0,2400.0,4797.0,11565.0,6272.0,31625.0,15876.0,5880.0,21250.0,2800.0,42250.0,6090.0,3510.0,6574.0,4560.0,15125.0,15000.0,7920.0,13912.0,6076.0,11000.0,9894.0,2929.0,23000.0,4346.0,22750.0,11990.0,1480.0,7644.0,1400.0,24500.0,7680.0,5535.0,7500.0,27000.0,4180.0,9936.0,2106.0,25000.0,13080.0,41250.0,2958.0,1998.0,4125.0,9656.0,11529.0,3332.0,1848.0,4136.0,8000.0,23625.0,32500.0,9660.0,28875.0,8250.0,3472.0,2520.0,21000.0,28750.0,21250.0,7875.0,1768.0,7216.0,3402.0,8750.0,21250.0,7375.0,13500.0,7056.0,24750.0,16250.0,9702.0,1989.0,1674.0,11475.0,2964.0,29250.0,10000.0,28750.0,6090.0,3690.0,1485.0,7650.0,20000.0,4576.0,5952.0,12250.0,8750.0,34125.0,28000.0,15000.0,1666.0,16875.0,6480.0,918.0,10455.0,28125.0,4008.0,10246.0,3500.0,13068.0,13530.0,2992.0,15000.0,1734.0,3536.0,8000.0,8372.0,1271.0,3901.0,23625.0,15000.0,5252.0,7000.0,21000.0,2700.0,20625.0,6300.0,3400.0,5709.0,8820.0,45000.0,5002.0,5180.0,4046.0,5456.0,6336.0,7268.0,3999.0,1450.0,1196.0,25500.0,15000.0,7791.0,3451.0,7095.0,33000.0,7700.0,3186.0,4212.0,28125.0,42250.0,13160.0,893.0,9600.0,16250.0,5632.0,12500.0,3750.0,11520.0,47850.0,984.0,3705.0,7500.0,13500.0,2516.0,8160.0,5160.0,17340.0,11000.0,3504.0,20800.0,6000.0,27000.0,3567.0,13440.0,2880.0,3460.0,9500.0,17000.0,5000.0,11327.0,3537.0,3468.0,6664.0,4752.0,10350.0,18750.0,5278.0,2368.0,29750.0,18000.0,4375.0,2862.0,4620.0,1368.0,4374.0,6696.0,7975.0,6500.0,30000.0,1680.0,5250.0,42000.0,9900.0,3243.0,1120.0,13804.0,19250.0,1850.0,18375.0,20000.0,15000.0,4508.0,4008.0,8750.0,1908.0,5830.0,5450.0,2448.0,7000.0,2108.0,2500.0,3432.0,10000.0,1406.0,6875.0,1134.0,11250.0,1755.0,7605.0,1500.0,14625.0,34500.0,3900.0,38750.0,2898.0,31500.0,2709.0,1650.0,10275.0,2160.0,14912.0,4356.0,6324.0,2925.0,3696.0,7020.0,2625.0,2080.0,9625.0,24000.0,9048.0,870.0,3500.0,23625.0,5565.0,10920.0,29250.0,12512.0,18000.0,5000.0,31625.0,11000.0,3564.0,45375.0,4356.0,12500.0,2480.0,6250.0,5811.0,4482.0,16250.0,12000.0,11250.0,5440.0,49875.0,6623.0,3675.0,33000.0,34375.0,8526.0,7650.0,2220.0,6072.0,22000.0,19920.0,2664.0,4740.0,16875.0,4200.0,24500.0,12000.0,14625.0,11802.0,10388.0,2850.0,14625.0,2162.0,3393.0,20000.0,10416.0,1885.0,48000.0,42250.0,8000.0,47125.0,42000.0,7888.0,16000.0,18000.0,6248.0,2430.0,4551.0,4455.0,6448.0,8000.0,4816.0,7500.0,12750.0,7245.0,13750.0,1537.0,1620.0,12144.0,6888.0,9735.0,4671.0,20625.0,8037.0,11092.0,20000.0,7956.0,7000.0,22500.0,13590.0,20250.0,14924.0,2028.0,4900.0,24750.0,8640.0,6875.0,5320.0,11250.0,4551.0,9280.0,6864.0,2000.0,2378.0,3312.0,1786.0,2460.0,46750.0,2156.0,29700.0,6250.0,4048.0,10575.0,14400.0,2600.0,2550.0,7579.0,6660.0,19840.0,7772.0,3344.0,4000.0,24750.0,5670.0,11684.0,5733.0,952.0,29250.0,15652.0,26125.0,2541.0,15750.0,31000.0,1767.0,7875.0,2989.0,6208.0,31500.0,5250.0,16250.0,8750.0,2146.0,5000.0,7936.0,4375.0,5208.0,9000.0,6250.0,19125.0,5250.0,6688.0,33250.0,4410.0,4182.0,10650.0,6669.0,672.0,2990.0,6000.0,2175.0,3848.0,25000.0,6396.0,9164.0,4056.0,3840.0,4524.0,6125.0,4485.0,2904.0,14625.0,13048.0,31250.0,3696.0,2080.0,1504.0,27500.0,10320.0,3564.0,27125.0,1950.0,13020.0,27625.0,18125.0,24000.0,3640.0,7144.0,5400.0,45500.0,4879.0,2214.0,19500.0,8942.0,1566.0,5000.0,3375.0,6000.0,3536.0,3276.0,6864.0,16875.0,1848.0,3384.0,2940.0,42000.0,5434.0,7000.0,27500.0,43750.0,3348.0,4608.0,2850.0,4862.0,2337.0,12750.0,1056.0,2184.0,1558.0,5248.0,1326.0,2014.0,5456.0,30250.0,16250.0,10998.0,5000.0,14625.0,1833.0,4472.0,5886.0,7820.0,28000.0,8000.0,6080.0,45375.0,18000.0,3360.0,4323.0,3090.0,11100.0,2112.0,14000.0,1444.0,11250.0,48000.0,2618.0,7875.0,11250.0,1564.0,3720.0,8208.0,17708.0,21250.0,23625.0,17136.0,5168.0,12250.0,4340.0,5512.0,6000.0,28875.0,17446.0,15272.0,7560.0,19250.0,2730.0,21125.0,7905.0,9350.0,1170.0,19500.0,18750.0,9280.0,12375.0,19500.0,3536.0,5460.0,7000.0,19250.0,2548.0,520.0,11016.0,23625.0,5508.0,48125.0,992.0,46250.0,4004.0,12500.0,24500.0,20250.0,14250.0,8550.0,14592.0,9520.0,1372.0,26250.0,10535.0,17875.0,10000.0,8750.0,7936.0,17500.0,22750.0,34500.0,3626.0,1558.0,7360.0,3584.0,6720.0,10252.0,7875.0,14850.0,45375.0,4648.0,3968.0,8729.0,7000.0,17118.0,2457.0,7216.0,19250.0,29750.0,4116.0,6975.0,3276.0,5000.0,10500.0,9322.0,530.0,3984.0,28000.0,1782.0,1404.0,3792.0,9000.0,18000.0,759.0,2684.0,4375.0,8750.0,13000.0,8694.0,4998.0,4452.0,4480.0,10125.0,17040.0,3668.0,10759.0,6875.0,35000.0,7865.0,9576.0,19500.0,9600.0,17875.0,15504.0,32375.0,13750.0,2850.0,45500.0,7296.0,4500.0,30000.0,20625.0,2530.0,28125.0,11124.0,3799.0,5512.0,2340.0,10116.0,29750.0,12500.0,11250.0,9570.0,7105.0,3750.0,8037.0,6591.0,13986.0,5175.0,2058.0,12000.0,3876.0,4560.0,6660.0,22500.0,1768.0,5886.0,3500.0,38250.0,42250.0,16500.0,3185.0,1716.0,5640.0,5805.0,28500.0,15000.0,21000.0,13500.0,5500.0,16320.0,1056.0,10000.0,13125.0,5103.0,10206.0,9263.0,2688.0,22500.0,1440.0,6125.0,13330.0,37500.0,8976.0,9856.0,34000.0,2120.0,18750.0,5616.0,7920.0,2788.0,8750.0,2407.0,6401.0,9450.0,3675.0,14943.0,10686.0,22500.0,6324.0,2964.0,37500.0,16500.0,14625.0,1976.0,21000.0,28000.0,28500.0,6000.0,4392.0,24500.0,7000.0,5760.0,34500.0,1482.0,6000.0,3536.0,6250.0,12597.0,6031.0,5194.0,1624.0,7548.0,16500.0,2256.0,2173.0,15872.0,5895.0,24500.0,7770.0,12375.0,1310.0,3332.0,7095.0,24750.0,16625.0,5250.0,4788.0,4462.0,1715.0,10500.0,2392.0,2900.0,36000.0,12000.0,11424.0,32000.0,27000.0,14573.0,3800.0,18375.0,42250.0,4200.0,9800.0,12750.0,9000.0,22000.0,5225.0,15750.0,46250.0,29000.0,12000.0,3640.0,8250.0,2475.0,2639.0,22500.0,15000.0,2548.0,14000.0,1764.0,10150.0,8733.0,8750.0,34875.0,13500.0,11899.0,2072.0,4914.0,6314.0,1548.0,16000.0,17000.0,4884.0,45000.0,660.0,21800.0,1428.0,2250.0,15125.0,3009.0,3564.0,4408.0,2380.0,2646.0,11055.0,25500.0,18375.0,13725.0,4368.0,9971.0,7672.0,10192.0,3185.0,4760.0,32000.0,5000.0,1584.0,12648.0,11178.0,17500.0,9890.0,9152.0,3696.0,33750.0,21250.0,34375.0,1512.0,33750.0,11375.0,4860.0,7812.0,23750.0,3948.0,4554.0,3096.0,6750.0,7912.0,12000.0,6438.0,11250.0,23625.0,5612.0,10000.0,5250.0,5959.0,4664.0,3604.0,28500.0,1344.0,10192.0,2926.0,1974.0,2745.0,5456.0,1800.0,9996.0,18000.0,25000.0,2844.0,19250.0,13932.0,11000.0,11375.0,2880.0,16124.0,37125.0,7770.0,49500.0,13500.0,29000.0,2856.0,19250.0,2100.0,49500.0,3672.0,1125.0,1672.0,22000.0,4480.0,5800.0,4890.0,3132.0,10535.0,9000.0,35750.0,14105.0,8554.0,2624.0,25875.0,18000.0,12768.0,7548.0,40000.0,5895.0,2828.0,4899.0,7943.0,5500.0,30000.0,25000.0,4446.0,19250.0,3597.0,1749.0,8330.0,11375.0,3332.0,24750.0,5670.0,3952.0,4998.0,2040.0,37125.0,6000.0,17875.0,8424.0,11628.0,1443.0,2250.0,16625.0,16625.0,27000.0,1053.0,7744.0,13750.0,5568.0,19500.0,10000.0,35000.0,9625.0,6426.0,18000.0,6150.0,12750.0,5152.0,2116.0,6360.0,2016.0,2108.0,12250.0,4032.0,36750.0,4160.0,3712.0,13000.0,11515.0,6000.0,7000.0,3822.0,4092.0,37125.0,3060.0,2275.0,3168.0,2147.0,7875.0,26125.0,9000.0,1702.0,4756.0,7875.0,1768.0,12500.0,15145.0,6200.0,5359.0,48000.0,3127.0,2079.0,810.0,6750.0,7000.0,15000.0,9646.0,27000.0,6300.0,2304.0,9800.0,4454.0,13920.0,4242.0,15300.0,37500.0,8151.0,5000.0,680.0,4375.0,1530.0,7000.0,6240.0,4200.0,42000.0,10000.0,22000.0,4921.0,1600.0,9494.0,24750.0,6572.0,2001.0,8120.0,4181.0,5704.0,6440.0,21000.0,4200.0,825.0,17100.0,1632.0,3481.0,4930.0,3072.0,13534.0,4158.0,8208.0,5000.0,27500.0,2310.0,2052.0,33750.0,1530.0,24750.0,24500.0,49500.0,6032.0,25500.0,11224.0,26250.0,6615.0,7168.0,23625.0,4293.0,8624.0,8430.0,3576.0,22000.0,7268.0,6171.0,12375.0,39875.0,31250.0,25000.0,1122.0,5014.0,4700.0,8360.0,23625.0,3652.0,8610.0,2185.0,5850.0,10560.0,8704.0,9450.0,6000.0,7500.0,16875.0,1421.0,28875.0,6250.0,8750.0,25875.0,2592.0,2220.0,2268.0,28875.0,20625.0,6000.0,3136.0,12896.0,6000.0,19500.0,10582.0,3600.0,5250.0,14016.0,8040.0,13000.0,7812.0,8928.0,26000.0,2496.0,31200.0,9750.0,4575.0,2940.0,4048.0,8748.0,17500.0,31500.0,33750.0,9744.0,2023.0,12558.0,14336.0,4500.0,11700.0,6250.0,11856.0,2560.0,7954.0,2006.0,2257.0,13500.0,4930.0,1628.0,8704.0,6004.0,6000.0,7440.0,7625.0,21125.0,3075.0,1056.0,5452.0,38250.0,6346.0,26125.0,10000.0,7182.0,13750.0,2494.0,24000.0,5658.0,8733.0,18000.0,5346.0,9000.0,4454.0,37125.0,8670.0,1275.0,3841.0,6240.0,6930.0,9971.0,14625.0,1584.0,2464.0,12375.0,12702.0,6760.0,598.0,5782.0,22000.0,3750.0,22000.0,6600.0,5440.0,16500.0,10000.0,9240.0,8866.0,5214.0,23375.0,4917.0,13750.0,14235.0,12000.0,7098.0,7155.0,1518.0,10050.0,44000.0,10000.0,7345.0,704.0,35700.0,3450.0,28500.0,2430.0,11250.0,6987.0,2376.0,26125.0,45000.0,25500.0,2712.0,30000.0,1833.0,1160.0,9064.0,4384.0,12375.0,3422.0,15125.0,6528.0,5658.0,5250.0,7875.0,1638.0,2520.0,9625.0,1029.0,7392.0,7439.0,11151.0,34125.0,4000.0,11250.0,22750.0,3128.0,1200.0,20000.0,27500.0,19125.0,13125.0,5250.0,17250.0,30000.0,4307.0,5292.0,2816.0,8415.0,15517.0,2184.0,4032.0,2709.0,32500.0,8750.0,21250.0,6912.0,38000.0,13038.0,15000.0,30250.0,5551.0,6888.0,37500.0,16500.0,5202.0,16875.0,21750.0,12750.0,20625.0,29000.0,2340.0,5250.0,1850.0,5250.0,2380.0,5600.0,5250.0,45000.0,5655.0,1551.0,2550.0,7056.0,12000.0,1188.0,9000.0,27500.0,5502.0,2838.0,7000.0,6321.0,38000.0,14664.0,7474.0,19250.0,18000.0,4277.0,31625.0,44000.0,986.0,3185.0,6670.0,5882.0,8155.0,2444.0,11322.0,11088.0,12152.0,30875.0,16560.0,5520.0,33750.0,32625.0,10019.0,12358.0,2002.0,7544.0,10086.0,6160.0,13750.0,8496.0,15750.0,4410.0,9048.0,2695.0,5130.0,6228.0,23000.0,15125.0,1892.0,3864.0,3360.0,1326.0,3680.0,3120.0,4719.0,48750.0,6000.0,4488.0,8131.0,22750.0,20250.0,42750.0,8710.0,20125.0,31500.0,12243.0,8625.0,6580.0,3478.0,3780.0,5488.0,4346.0,5880.0,744.0,28750.0,4316.0,18375.0,5300.0,8109.0,4060.0,5875.0,4212.0,4536.0,4370.0,27000.0,7890.0,2106.0,3355.0,5796.0,4131.0,49500.0,658.0,2821.0,4508.0,5876.0,1972.0,8004.0,3825.0,4633.0,12716.0,16250.0,5355.0,8700.0,2640.0,2900.0,2552.0,20349.0,16789.0,7000.0,9916.0,8246.0,4500.0,46500.0,9581.0,12500.0,2156.0,6000.0,6552.0,6720.0,12291.0,13500.0,8460.0,37125.0,22000.0,33750.0,28875.0,16250.0,6500.0,6579.0,10320.0,420.0,20625.0,6466.0,1974.0,4144.0,16875.0,21750.0,4932.0,17500.0,1404.0,4000.0,13000.0,9000.0,4500.0,1224.0,4173.0,6400.0,2277.0,9000.0,1696.0,12250.0,26250.0,17000.0,3922.0,3290.0,16625.0,2842.0,10000.0,4794.0,6027.0,7656.0,37500.0,28875.0,1596.0,1586.0,7875.0,7514.0,16875.0,34125.0,37125.0,3182.0,15000.0,15552.0,3174.0,18750.0,3052.0,6006.0,6265.0,4582.0,42625.0,10241.0,16500.0,7500.0,18000.0,6424.0,2583.0,24375.0,1802.0,4560.0,5796.0,42500.0,4488.0,6783.0,2400.0,5625.0,17800.0,8777.0,9375.0,48750.0,5934.0,6478.0,16500.0,12000.0,1798.0,3900.0,30875.0,33750.0,9261.0,5751.0,17875.0,5863.0,3740.0,2583.0,6902.0,2112.0,30000.0,880.0,14875.0,1305.0,9520.0,16250.0,16500.0,11803.0,8619.0,2990.0,11375.0,38500.0,13000.0,12276.0,12800.0,1764.0,1920.0,16500.0,9000.0,11550.0,20125.0,38000.0,9990.0,24525.0,27000.0,17500.0,2730.0,2610.0,40000.0,6860.0,7310.0,25500.0,8294.0,4887.0,21375.0,29750.0,15000.0,20100.0,7371.0,1296.0,22500.0,13000.0,24375.0,960.0,4719.0,4992.0,1512.0,42625.0,6279.0,736.0,8478.0,16926.0,3726.0,44000.0,40000.0,12000.0,17500.0,8250.0,7144.0,23375.0,34875.0,13734.0,2806.0,2376.0,5504.0,12000.0,8621.0,20000.0,8100.0,45000.0,2244.0,9625.0,7029.0,1938.0,4888.0,4563.0,27000.0,1540.0,22750.0,14018.0,29750.0,5964.0,3402.0,3120.0,3375.0,25000.0,19500.0,6750.0,10336.0,14875.0,3476.0,8272.0,8858.0,3525.0,15552.0,4680.0,16500.0,7000.0,33000.0,1330.0,2997.0,31250.0,6450.0,5940.0,46750.0,3510.0,6880.0,4674.0,5000.0,4998.0,16250.0,6837.0,13603.0,3528.0,3936.0,34375.0,4224.0,4500.0,722.0,1376.0,25000.0,1386.0,3828.0,6486.0,3381.0,9800.0,7875.0,1551.0,12642.0,6325.0,31500.0,946.0,12000.0,6192.0,3619.0,9625.0,23750.0,14625.0,18000.0,2465.0,4150.0,6396.0,840.0,8968.0,6450.0,6510.0,1920.0,8250.0,2625.0,7500.0,12006.0,2501.0,6440.0,4508.0,22500.0,5000.0,10045.0,12375.0,2950.0,9408.0,2156.0,14784.0,7744.0,4995.0,15288.0,5000.0,6720.0,7038.0,11591.0,2728.0,7560.0,3650.0,4125.0,5500.0,2323.0,26000.0,8316.0,4160.0,7875.0,11000.0,2625.0,6780.0,1458.0,10994.0,6750.0,13750.0,19125.0,8496.0,7943.0,26000.0,8211.0,14322.0,6560.0,4356.0,4884.0,3625.0,1672.0,42000.0,7074.0,7875.0,10948.0,12558.0,2376.0,9744.0,15125.0,10206.0,3552.0,5481.0,22500.0,10325.0,2940.0,10000.0,3744.0,7875.0,9996.0,19500.0,8250.0,3720.0,1584.0,5684.0,16875.0,5593.0,4375.0,5795.0,8385.0,5928.0,1551.0,12672.0,5440.0,2496.0,43875.0,40500.0,2898.0,18000.0,8000.0,5192.0,450.0,16500.0,6750.0,13939.0,3003.0,17500.0,1128.0,2070.0,1500.0,10000.0,2400.0,45000.0,5600.0,7875.0,20000.0,31500.0,9348.0,1496.0,39000.0,7875.0,12500.0,5292.0,10464.0,8007.0,45500.0,1596.0,15000.0,1008.0,2832.0,8190.0,32500.0,10500.0,38500.0,16500.0,10656.0,1476.0,1326.0,5376.0,3000.0,1305.0,6000.0,8240.0,7500.0,2448.0,22500.0,13125.0,6000.0,8388.0,2210.0,24750.0,4794.0,3321.0,11368.0,10906.0,3608.0,6240.0,2623.0,7913.0,18000.0,609.0,7040.0,1794.0,40000.0,21000.0,5593.0,2639.0,37375.0,12960.0,11977.0,11656.0,5846.0,7800.0,2616.0,3528.0,4560.0,7192.0,840.0,4416.0,5400.0,936.0,2320.0,1435.0,4900.0,27500.0,10500.0,6762.0,3168.0,575.0,28125.0,4172.0,11362.0,18000.0,10416.0,6943.0,11544.0,24500.0,6750.0,21875.0,16875.0,6670.0,34375.0,15392.0,4158.0,12950.0,5136.0,6944.0,9165.0,1092.0,13050.0,25875.0,29250.0,14625.0,3648.0,9000.0,45500.0,1496.0,5096.0,22000.0,38675.0,9120.0,19250.0,17875.0,49000.0,1908.0,9625.0,16250.0,513.0,10000.0,900.0,7105.0,1386.0,45000.0,12500.0,16393.0,39000.0,8250.0,37500.0,4554.0,9625.0,36250.0,27500.0,5882.0,6496.0,4455.0,9768.0,2478.0,5000.0,23375.0,21250.0,5724.0,12250.0,3655.0,4940.0,5625.0,4760.0,16875.0,15750.0,7670.0,10569.0,16875.0,7581.0,13125.0,11620.0,3825.0,5976.0,8316.0,2650.0,14025.0,21375.0,17000.0,31000.0,4608.0,7682.0,36000.0,43875.0,11446.0,7500.0,18375.0,5568.0,2212.0,8370.0,22500.0,5757.0,3549.0,2376.0,4080.0,39000.0,5824.0,8250.0,47125.0,12800.0,44625.0,754.0,3960.0,7410.0,6328.0,2584.0,2850.0,27625.0,17325.0,3321.0,15125.0,1530.0,4200.0,4437.0,782.0,3059.0,5037.0,1000.0,5145.0,9672.0,2700.0,5904.0,40000.0,13750.0,28125.0,10840.0,25875.0,28875.0,10500.0,12936.0,9375.0,5513.0,3000.0,25000.0,2585.0,2788.0,24000.0,1944.0,3780.0,20000.0,4071.0,33000.0,6090.0,9672.0,3456.0,13000.0,6250.0,16625.0,13020.0,7920.0,5148.0,2464.0,377.0,6496.0,26250.0,6419.0,21375.0,12500.0,3465.0,33000.0,16500.0,2576.0,19500.0,6572.0,20125.0,5577.0,6400.0,9660.0,16000.0,8460.0,3120.0,47500.0,5502.0,2451.0,12150.0,24375.0,693.0,5831.0,9625.0,3996.0,12638.0,7943.0,2352.0,16250.0,6004.0,7452.0,8372.0,8385.0,7526.0,5775.0,8000.0,3360.0,4056.0,10036.0,1920.0,27000.0,13750.0,7266.0,12300.0,7308.0,2048.0,8836.0,9750.0,29750.0,25500.0,4875.0,3354.0,10199.0,4590.0,357.0,15000.0,10080.0,7293.0,3596.0,5202.0,13000.0,589.0,18615.0,9625.0,30375.0,1452.0,12000.0,3300.0,37125.0,5664.0,5863.0,2175.0,9984.0,9675.0,4664.0,8584.0,4277.0,17000.0,12688.0,3328.0,35750.0,35000.0,10725.0,6250.0,3648.0,5184.0,30000.0,15834.0,7749.0,38750.0,4368.0,27000.0,37500.0,19125.0,42500.0,16500.0,3948.0,10290.0,28875.0,17355.0,3655.0,16463.0,4641.0,3876.0,10125.0,1927.0,7488.0,4375.0,40000.0,19125.0,8092.0,33750.0,4872.0,2185.0,825.0,9240.0,42625.0,45500.0,6272.0,3277.0,12152.0,17500.0,3780.0,2040.0,7875.0,6072.0,19593.0,5425.0,6600.0,11102.0,34125.0,1643.0,2772.0,1248.0,5616.0,6825.0,4864.0,49500.0,2448.0,1380.0,11850.0,3900.0,46750.0,5310.0,20125.0,930.0,9600.0,3075.0,22500.0,10414.0,40000.0,3393.0,11730.0,10752.0,7965.0,3258.0,2862.0,4698.0,16500.0,6300.0,2700.0,36250.0,42750.0,4500.0,1672.0,6000.0,2352.0,9156.0,5236.0,28125.0,6875.0,8750.0,3450.0,15000.0,27500.0,33750.0,12375.0,45000.0,5589.0,7500.0,3145.0,4872.0,2538.0,18750.0,14076.0,4602.0,6768.0,2025.0,7200.0,8190.0,2419.0,10788.0,6125.0,3243.0,4136.0,20625.0,7000.0,16104.0,3122.0,26250.0,30000.0,2856.0,4620.0,2595.0,12354.0,25500.0,6048.0,4641.0,4256.0,3750.0,10738.0,19000.0,35750.0,5625.0,437.0,15625.0,20625.0,4687.0,32625.0,11844.0,8791.0,17215.0,3870.0,49500.0,9212.0,10125.0,10431.0,6250.0,29975.0,1806.0,8250.0,24375.0,21375.0,10105.0,2080.0,9625.0,28875.0,960.0,30875.0,8103.0,2808.0,17500.0,2310.0,2464.0,6875.0,4851.0,2044.0,19500.0,3311.0,10000.0,22000.0,5130.0,1425.0,2145.0,14000.0,13125.0,11760.0,10125.0,10125.0,1498.0,21375.0,880.0,2964.0,15125.0,4375.0,4263.0,4576.0,1568.0,4752.0,7000.0,6435.0,7599.0,3744.0,5600.0,10320.0,7366.0,15750.0,5610.0,11456.0,3690.0,40500.0,7293.0,4472.0,31500.0,3640.0,4590.0,39000.0,1725.0,12364.0,10011.0,46875.0,15872.0,6650.0,3800.0,9061.0,8250.0,36750.0,4578.0,6578.0,11760.0,29250.0,1224.0,910.0,26250.0,27500.0,9610.0,16500.0,42250.0,3948.0,2378.0,1855.0,5000.0,4320.0,19500.0,2697.0,36000.0,9129.0,11250.0,11000.0,8125.0,8768.0,42625.0,1232.0,5850.0,2623.0,5934.0,10500.0,3780.0,1404.0,2856.0,11554.0,8580.0,2340.0,5336.0,4320.0,7500.0,3458.0,7500.0,6929.0,7958.0,2196.0,10212.0,3016.0,14455.0,5292.0,9588.0,13464.0,5625.0,2091.0,3844.0,3071.0,5670.0,6000.0,2516.0,7875.0,24750.0,4641.0,29250.0,1242.0,14448.0,8750.0,8036.0,12320.0,6750.0,2496.0,4284.0,3230.0,5069.0,9625.0,20125.0,12427.0,24500.0,392.0,6912.0,31250.0,15000.0,6235.0,10880.0,2147.0,21250.0,11000.0,3591.0,9604.0,9936.0,330.0,11000.0,38000.0,12596.0,3861.0,3441.0,21125.0,4500.0,12936.0,3210.0,2014.0,32500.0,5928.0,5289.0,14875.0,8789.0,7708.0,20625.0,6370.0,3780.0,21000.0,5888.0,7125.0,17875.0,4500.0,15000.0,2961.0,7332.0,4816.0,4418.0,6390.0,20250.0,40000.0,11000.0,11648.0,27500.0,5586.0,2912.0,728.0,5504.0,4200.0,2500.0,28125.0,18750.0,3306.0,11475.0,4116.0,21375.0,2639.0,16500.0,25500.0,1175.0,11883.0,4800.0,1998.0,1711.0,40000.0,5593.0,3000.0,3330.0,25875.0,36000.0,6125.0,6000.0,5040.0,7875.0,5670.0,10234.0,2592.0,15125.0,1786.0,2142.0,9750.0,12750.0,12349.0,37500.0,2940.0,6696.0,12500.0,2553.0,7420.0,46500.0,18800.0,9212.0,4375.0,1728.0,5439.0,27000.0,4872.0,28750.0,8835.0,30000.0,13000.0,2656.0,15792.0,3306.0,8820.0,7426.0,2331.0,6783.0,5824.0,32500.0,7680.0,34375.0,7728.0,10000.0,8750.0,20000.0,2296.0,4500.0,22750.0,23000.0,1739.0,8750.0,16250.0,10125.0,1800.0,6528.0,2496.0,16400.0,9842.0,2816.0,18000.0,7742.0,2784.0,6992.0,4410.0,4002.0,9184.0,5320.0,3822.0,26000.0,2250.0,10000.0,11232.0,10290.0,5456.0,2312.0,24500.0,9750.0,10125.0,6439.0,12880.0,6380.0,1392.0,12500.0,9288.0,7920.0,16250.0,2665.0,22000.0,3854.0,22750.0,4914.0,14625.0,22250.0,2448.0,819.0,924.0,30000.0,36000.0,20000.0,16500.0,17500.0,1728.0,5454.0,13500.0,13750.0,11000.0,2880.0,27000.0,9064.0,21375.0,11466.0,11856.0,42000.0,2448.0,6794.0,5364.0,10000.0,4500.0,10000.0,2800.0,12800.0,19250.0,40250.0,5994.0,5475.0,19250.0,27000.0,1357.0,10560.0,7488.0,5066.0,7875.0,46000.0,6132.0,2014.0,1360.0,4136.0,1836.0,28875.0,2484.0,8250.0,2184.0,4992.0,8642.0,8854.0,2112.0,49500.0,1056.0,16875.0,27500.0,3750.0,3567.0,5125.0,5775.0,1485.0,5474.0,2660.0,6407.0,4859.0,2244.0,9000.0,10350.0,3465.0,4410.0,10000.0,3075.0,1978.0,41250.0,3441.0,11920.0,28000.0,9799.0,7500.0,33000.0,3427.0,30375.0,24000.0,29250.0,1053.0,3655.0,9282.0,5625.0,6000.0,5876.0,16250.0,2100.0,43500.0,36250.0,22500.0,10000.0,7040.0,48875.0,2464.0,7410.0,1587.0,3990.0,10504.0,1845.0,13000.0,1170.0,18000.0,2352.0,23625.0,9000.0,2940.0,1875.0,7260.0,13160.0,13500.0,4620.0,19500.0,9936.0,13500.0,2652.0,6360.0,7000.0,5043.0,4200.0,3588.0,7956.0,13000.0,7644.0,4788.0,1500.0,38000.0,1911.0,4719.0,6768.0,4800.0,12218.0,24500.0,3915.0,13500.0,14280.0,6006.0,11832.0,3360.0,11025.0,46250.0,4576.0,5440.0,16625.0,9625.0,12972.0,3876.0,18750.0,1900.0,1833.0,8190.0,8496.0,36250.0,19500.0,23625.0,3510.0,40500.0,16776.0,3392.0,2816.0,2980.0,5076.0,5960.0,38500.0,15000.0,15125.0,1464.0,2997.0,33000.0,1938.0,11000.0,5208.0,9440.0,4941.0,5148.0,6785.0,49000.0,10816.0,6784.0,4407.0,6612.0,35750.0,893.0,7688.0,8532.0,47125.0,3720.0,5792.0,11610.0,9000.0,2640.0,4160.0,2548.0,2980.0,4181.0,4500.0,6600.0,11700.0,4488.0,15000.0,36250.0,11250.0,4700.0,1326.0,3808.0,14625.0,41250.0,12116.0,2068.0,36000.0,10000.0,24750.0,35625.0,41250.0,32700.0,44000.0,11960.0,23375.0,31500.0,2241.0,5670.0,2808.0,6786.0,8313.0,7000.0,4500.0,11324.0,3080.0,3906.0,9450.0,1258.0,5662.0,6125.0,39000.0,3029.0,3861.0,9180.0,46250.0,5376.0,35000.0,9000.0,34375.0,10208.0,1102.0,20000.0,4218.0,6000.0,4756.0,6747.0,19125.0,4375.0,8000.0,3427.0,3360.0,40375.0,7896.0,5952.0,6900.0,4250.0,1140.0,1216.0,8507.0,3008.0,16500.0,4968.0,1312.0,2232.0,9000.0,31250.0,6929.0,26125.0,8575.0,5510.0,1064.0,7223.0,3439.0,3120.0,30000.0,34375.0,4050.0,1176.0,1558.0,44000.0,495.0,25000.0,21000.0,23000.0,7280.0,2952.0,17000.0,1632.0,8624.0,15962.0,4158.0,8575.0,22500.0,3840.0,12954.0,5985.0,10500.0,12000.0,7958.0,5434.0,11214.0,1850.0,2204.0,14616.0,4212.0,38750.0,4700.0,6300.0,12032.0,6000.0,3375.0,19250.0,8432.0,13500.0,7448.0,27000.0,2448.0,6554.0,9225.0,6160.0,3220.0,7140.0,1875.0,2728.0,6468.0,3915.0,10780.0,8378.0,3552.0,3174.0,1353.0,2401.0,3483.0,6059.0,6396.0,16500.0,3857.0,7875.0,10368.0,6875.0,6750.0,3050.0,1457.0,22500.0,24000.0,35750.0,28000.0,4033.0,25000.0,11000.0,10750.0,2806.0,18375.0,8858.0,14250.0,2862.0,44625.0,3580.0,3248.0,12500.0,30000.0,7500.0,43125.0,36000.0,2368.0,1334.0,9204.0,14616.0,13500.0,20625.0,5076.0,12375.0,6272.0,680.0,2860.0,4403.0,12928.0,7050.0,24750.0,14742.0,7105.0,5280.0,20000.0,9625.0,26250.0,1900.0,3930.0,3822.0,5640.0,24000.0,7865.0,8125.0,40000.0,23625.0,11352.0,6076.0,15750.0,4030.0,6496.0,3240.0,20250.0,5555.0,7326.0,5358.0,1856.0,7680.0,5192.0,1512.0,4671.0,5002.0,3712.0,2646.0,7920.0,16500.0,2772.0,12000.0,13500.0,1749.0,2701.0,5460.0,26250.0,5940.0,14875.0,8155.0,12546.0,8526.0,13464.0,7000.0,4945.0,21000.0,3900.0,2772.0,2958.0,9971.0,6847.0,1887.0,7875.0,29250.0,3219.0,6272.0,3075.0,8460.0,5662.0,5625.0,2806.0,6080.0,22750.0,1480.0,9750.0,10556.0,1786.0,20000.0,16500.0,18750.0,4399.0,13860.0,4320.0,3159.0,3597.0,6875.0,6480.0,10720.0,4719.0,4500.0,5076.0,27000.0,27000.0,7840.0,4264.0,2052.0,11475.0,2760.0,4416.0,646.0,3087.0,4872.0,23000.0,48125.0,17500.0,4664.0,3211.0,8850.0,4375.0,1560.0,30250.0,15568.0,2960.0,4060.0,8750.0,15000.0,11324.0,19250.0,7875.0,18000.0,11250.0,8855.0,6248.0,10125.0,20250.0,3887.0,3840.0,3304.0,1955.0,28750.0,8832.0,1645.0,42625.0,12500.0,9541.0,42000.0,1064.0,45000.0,39875.0,6794.0,6750.0,7956.0,3190.0,2968.0,13125.0,8575.0,15000.0,3422.0,35000.0,48750.0,5408.0,10000.0,9750.0,3330.0,3234.0,24500.0,2990.0,8750.0,2816.0,13717.0,3311.0,1350.0,41250.0,3901.0,32500.0,13668.0,15750.0,1044.0,20625.0,1786.0,26000.0,15125.0,24750.0,10872.0,19250.0,15000.0,6250.0,9625.0,8729.0,6000.0,7875.0,16500.0,6916.0,4551.0,7000.0,6125.0,8096.0,22000.0,3634.0,1500.0,13500.0,11025.0,13000.0,2380.0,7943.0,16625.0,11072.0,5612.0,6640.0,10080.0,10458.0,4674.0,7520.0,3248.0,1216.0,10325.0,19500.0,10500.0,2173.0,14000.0,5586.0,30000.0,6084.0,16500.0,37000.0,22750.0,17500.0,15000.0,22500.0,11000.0,10952.0,962.0,3483.0,10360.0,10856.0,10252.0,5751.0,3749.0,16500.0,6642.0,6750.0,5250.0,10773.0,3276.0,4992.0,4756.0,3886.0,7650.0,4960.0,4482.0,7854.0,1824.0,14384.0,6875.0,2541.0,30000.0,2700.0,2014.0,2623.0,1176.0,29800.0,2700.0,3472.0,4147.0,1050.0,2091.0,6948.0,27500.0,3630.0,16875.0,3366.0,46750.0,4230.0,14000.0,11130.0,2701.0,9204.0,4879.0,1952.0,3360.0,32500.0,19250.0,24000.0,5856.0,24000.0,5000.0,4180.0,2340.0,17280.0,20000.0,2618.0,4606.0,9280.0,42750.0,4848.0,2128.0,875.0,5160.0,8056.0,40000.0,7304.0,19000.0,15050.0,13500.0,6250.0,34200.0,6250.0,24500.0,4935.0,19500.0,42750.0,3277.0,4096.0,1872.0,9375.0,10017.0,2000.0,10008.0,7748.0,9212.0,3075.0,7500.0,8000.0,13860.0,2080.0,5130.0,27000.0,4293.0,38500.0,1452.0,21125.0,7682.0,14080.0,4446.0,8750.0,1482.0,1081.0,5811.0,5250.0,8195.0,9625.0,5724.0,14375.0,43750.0,2331.0,6419.0,1216.0,6012.0,2782.0,7514.0,28125.0,11446.0,5292.0,13000.0,39875.0,4840.0,26250.0,8850.0,13158.0,4480.0,6588.0,12500.0,9600.0,880.0,5000.0,1584.0,3034.0,43875.0,4459.0,15750.0,6240.0,48875.0,30250.0,15750.0,9898.0,11550.0,26800.0,2093.0,5625.0,12375.0,12375.0,4242.0,33000.0,3256.0,1683.0,1872.0,10127.0,2590.0,35750.0,25000.0,1786.0,15750.0,9625.0,6345.0,21125.0,16254.0,3480.0,20000.0,34375.0,2340.0,22750.0,7371.0,8125.0,2070.0,24750.0,5005.0,12000.0,15750.0,11025.0,33750.0,5369.0,12600.0,13750.0,7680.0,5365.0,9750.0,21375.0,9490.0,1944.0,5900.0,12150.0,12375.0,6125.0,6975.0,19500.0,13066.0,7290.0,42000.0,31625.0,36000.0,665.0,5625.0,26250.0,6461.0,47500.0,10500.0,18000.0,16250.0,18900.0,1224.0,8723.0,8750.0,10452.0,3596.0,37500.0,11362.0,2220.0,4802.0,3304.0,21375.0,16520.0,6520.0,23250.0,17875.0,11250.0,2844.0,6179.0,7682.0,23375.0,15000.0,3364.0,4725.0,10010.0,46125.0,13157.0,13000.0,4876.0,21663.0,2392.0,11250.0,20145.0,22000.0,6120.0,5460.0,49000.0,5017.0,22750.0,6916.0,6000.0,11875.0,2296.0,5733.0,5625.0,3920.0,4292.0,5904.0,836.0,1500.0,11250.0,1674.0,6820.0,1122.0,10000.0,10752.0,7849.0,3332.0,6160.0,4500.0,6256.0,19250.0,9177.0,11550.0,7772.0,7040.0,15120.0,7301.0,15750.0,4864.0,8820.0,29250.0,3320.0,3567.0,32500.0,7875.0,3774.0,5580.0,22500.0,9240.0,2772.0,30000.0,1804.0,5500.0,4988.0,9792.0,20625.0,6440.0,6336.0,10304.0,12006.0,14725.0,12648.0,28750.0,30375.0,19125.0,14398.0,5145.0,14080.0,1679.0,3822.0,3102.0,21125.0,4704.0,5029.0,5191.0,1440.0,18000.0,4410.0,2889.0,9000.0,22000.0,2688.0,9300.0,3380.0,6908.0,11550.0,18000.0,17447.0,5208.0,4343.0,4116.0,15477.0,6086.0,3927.0,8750.0,20250.0,984.0,18788.0,11550.0,49500.0,2093.0,2829.0,3431.0,1265.0,6272.0,6850.0,7360.0,18000.0,1610.0,8000.0,15000.0,8686.0,6372.0,3120.0,3145.0,6215.0,13500.0,13050.0,10296.0,8850.0,8500.0,2024.0,6392.0,7840.0,7875.0,3825.0,585.0,5550.0,1950.0,1806.0,16250.0,27000.0,6510.0,2880.0,3168.0,6422.0,3552.0,6125.0,3150.0,25000.0,22750.0,16133.0,1632.0,7875.0,8424.0,2000.0,6440.0,4736.0,2905.0,3549.0,3248.0,4186.0,8250.0,1736.0,49000.0,9625.0,15104.0,4646.0,11000.0,1225.0,25500.0,18000.0,17472.0,10712.0,4092.0,15750.0,15225.0,10062.0,26125.0,6468.0,5184.0,18750.0,10125.0,17000.0,7504.0,39000.0,11875.0,675.0,19500.0,6500.0,8580.0,39875.0,6000.0,20000.0,2193.0,25500.0,1116.0,27625.0,5130.0,26250.0,4305.0,1856.0,9750.0,8000.0,5846.0,2280.0,35750.0,7680.0,9000.0,17358.0,23625.0,9000.0,3510.0,2926.0,12432.0,8125.0,16625.0,1776.0,8153.0,2024.0,3720.0,38250.0,9894.0,20250.0,1260.0,17875.0,5215.0,11400.0,3567.0,27000.0,4480.0,31250.0,4760.0,10125.0,5100.0,6344.0,33000.0,6390.0,3403.0,8083.0,4536.0,10404.0,1764.0,16317.0,4340.0,5880.0,5194.0,28750.0,9486.0,19500.0,7670.0,4505.0,5481.0,11600.0,5206.0,2990.0,4200.0,960.0,2440.0,6200.0,6045.0,31500.0,13500.0,5670.0,2850.0,1682.0,7380.0,2640.0,48750.0,2829.0,9380.0,41250.0,33250.0,1875.0,10000.0,4750.0,9450.0,2784.0,14000.0,6105.0,2430.0,15000.0,4950.0,15375.0,7500.0,4181.0,10000.0,3645.0,9633.0,16000.0,3042.0,19250.0,6854.0,6768.0,6784.0,1702.0,5945.0,3567.0,21000.0,7222.0,4000.0,11250.0,4794.0,3360.0,6375.0,5200.0,2652.0,21375.0,10863.0,12870.0,25500.0,35750.0,17094.0,4847.0,15125.0,10140.0,16875.0,4469.0,4410.0,20000.0,1092.0,4662.0,8610.0,4266.0,26250.0,2928.0,2850.0,6580.0,10125.0,3876.0,30875.0,7155.0,12155.0,14000.0,8610.0,2184.0,7500.0,11250.0,4125.0,2940.0,10614.0,23000.0,2268.0,8575.0,31625.0,5311.0,18850.0,4488.0,34375.0,3078.0,4368.0,4950.0,3248.0,2625.0,6150.0,4615.0,37125.0,42000.0,11250.0,8000.0,16875.0,34500.0,42000.0,1860.0,9100.0,26000.0,10472.0,3234.0,6678.0,3696.0,9000.0,48000.0,18000.0,28500.0,9568.0,2500.0,16250.0,7172.0,5586.0,4505.0,25000.0,5500.0,2408.0,5250.0,32000.0,33000.0,7840.0,5618.0,4116.0,3344.0,581.0,9400.0,10000.0,20000.0,6432.0,8883.0,40000.0,3680.0,39000.0,7820.0,5104.0,8250.0,27500.0,4620.0,3654.0,2500.0,12096.0,2080.0,8112.0,1408.0,7500.0,4896.0,11250.0,1672.0,10659.0,5460.0,8000.0,8750.0,26000.0,4560.0,23750.0,26250.0,24000.0,5427.0,1232.0,6000.0,7700.0,5624.0,13750.0,45375.0,3444.0,14375.0,38500.0,9087.0,5555.0,1482.0,10064.0,5500.0,7875.0,10370.0,9646.0,8750.0,13600.0,21000.0,8505.0,13500.0,11025.0,15000.0,1968.0,49500.0,8000.0,1000.0,2958.0,2880.0,6448.0,20000.0,7500.0,26000.0,10218.0,3161.0,9593.0,2820.0,5216.0,24800.0,27000.0,6720.0,10584.0,2325.0,9499.0,3708.0,9435.0,6188.0,10496.0,26125.0,18125.0,31250.0,15125.0,5684.0,4550.0,5292.0,5250.0,4096.0,23500.0,2822.0,48000.0,9000.0,31625.0,26125.0,24750.0,2960.0,15190.0,2107.0,21375.0,8750.0,9379.0,2645.0,6615.0,24750.0,9384.0,13500.0,7722.0,2520.0,8965.0,4218.0,36000.0,5684.0,4250.0,40000.0,10500.0,17875.0,4875.0,35750.0,23375.0,9405.0,1292.0,27000.0,30000.0,6346.0,15768.0,5191.0,2701.0,12992.0,45500.0,36000.0,5180.0,42625.0,11026.0,5012.0,33000.0,25000.0,3724.0,15000.0,11008.0,5800.0,1131.0,27000.0,1512.0,7700.0,952.0,5811.0,1280.0,8750.0,12036.0,1768.0,3750.0,31275.0,12000.0,18750.0,2379.0,13500.0,3900.0,7500.0,2028.0,45000.0,29750.0,45500.0,3420.0,23750.0,14000.0,20250.0,12960.0,33750.0,1596.0,4500.0,17875.0,28000.0,24750.0,2701.0,2997.0,580.0,7074.0,3750.0,11000.0,4186.0,11000.0,2079.0,6000.0,12750.0,23625.0,4950.0,2442.0,47500.0,9366.0,13750.0,9625.0,12000.0,21250.0,17500.0,9480.0,1122.0,6549.0,7448.0,6750.0,3132.0,8541.0,13440.0,14625.0,23625.0,2352.0,5440.0,5145.0,48150.0,47125.0,7056.0,7426.0,2992.0,10290.0,7095.0,6256.0,1740.0,26250.0,19344.0,42625.0,21250.0,2176.0,21250.0,5280.0,14250.0,6468.0,8658.0,6656.0,3296.0,3549.0,11264.0,5412.0,8362.0,16000.0,11924.0,12375.0,3570.0,16625.0,26250.0,16500.0,2684.0,2331.0,7293.0,5625.0,4611.0,4205.0,21875.0,6200.0,8064.0,8806.0,41000.0,25500.0,2850.0,4375.0,3144.0,3108.0,4482.0,20625.0,11648.0,7800.0,1512.0,3264.0,8775.0,1936.0,5625.0,8750.0,6000.0,48000.0,8379.0,6235.0,13321.0,1479.0,2625.0,19500.0,3780.0,8478.0,3808.0,12375.0,5742.0,6144.0,8384.0,9450.0,46250.0,4060.0,4500.0,42500.0,22000.0,10000.0,3634.0,12788.0,11468.0,10976.0,45500.0,10000.0,40500.0,1862.0,2625.0,7840.0,4824.0,594.0,13632.0,7920.0,22750.0,15575.0,3120.0,12987.0,4321.0,2448.0,27000.0,2500.0,6372.0,2788.0,6987.0,5508.0,4998.0,1494.0,3852.0,936.0,36000.0,2262.0,7450.0,3240.0,2424.0,1771.0,1568.0,8000.0,13500.0,1892.0,17875.0,42750.0,29250.0,1564.0,1377.0,1444.0,4896.0,7500.0,6272.0,8610.0,10476.0,9375.0,16225.0,3402.0,6750.0,11232.0,4800.0,3026.0,12489.0,12120.0,12375.0,1218.0,25000.0,10944.0,3432.0,2312.0,4914.0,45500.0,7000.0,8036.0,6893.0,42250.0,7260.0,3060.0,7980.0,4620.0,3648.0,2318.0,5950.0,3478.0,5043.0,7260.0,5876.0,31625.0,3504.0,4301.0,8150.0,2296.0,8750.0,37000.0,9625.0,5508.0,8268.0,5530.0,49500.0,4788.0,2550.0,9000.0,22000.0,11000.0,7875.0,8442.0,5168.0,31875.0,2666.0,5184.0,3672.0,32500.0,2100.0,38000.0,1368.0,6576.0,12582.0,6750.0,10080.0,3330.0,2691.0,2204.0,10764.0,6794.0,3036.0,1927.0,6825.0,27225.0,3895.0,5106.0,34000.0,19500.0,6450.0,16800.0,8308.0,33250.0,5000.0,1728.0,5400.0,14875.0,32500.0,4901.0,4131.0,30000.0,8750.0,40000.0,10019.0,4454.0,13000.0,10000.0,5973.0,11880.0,4872.0,28875.0,5549.0,6110.0,1089.0,35750.0,16625.0,22250.0,5236.0,8000.0,5365.0,5130.0,3225.0,910.0,25000.0,6640.0,6000.0,31500.0,4914.0,31875.0,9541.0,7800.0,3960.0,2450.0,4080.0,4732.0,2262.0,12818.0,4329.0,7560.0,8424.0,15750.0,4500.0,9000.0,36000.0,8064.0,11250.0,3472.0,25000.0,13750.0,11200.0,6435.0,870.0,2183.0,10752.0,28000.0,5424.0,28500.0,1403.0,28000.0,15624.0,1836.0,984.0,15000.0,16875.0,2176.0,16500.0,2739.0,5966.0,3904.0,7740.0,37375.0,1512.0,29975.0,10125.0,13000.0,3009.0,4611.0,26000.0,7380.0,27500.0,3616.0,4860.0,4914.0,26000.0,22500.0,1188.0,2925.0,1920.0,2929.0,6208.0,21375.0,48125.0,26950.0,42750.0,3074.0,18750.0,1752.0,1232.0,9000.0,6700.0,12000.0,12000.0,2320.0,1872.0,3799.0,37500.0,28750.0,11000.0,22500.0,3536.0,2465.0,18972.0,4992.0,28500.0,6765.0,2592.0,9548.0,8500.0,10982.0,5536.0,37125.0,15390.0,3000.0,9639.0,32500.0,19125.0,2106.0,22500.0,9000.0,12375.0,2160.0,6875.0,14637.0,11005.0,1189.0,13771.0,3780.0,2142.0,9240.0,7080.0,22454.0,4000.0,4914.0,2784.0,1920.0,18403.0,47125.0,3910.0,20800.0,12375.0,2325.0,13000.0,16500.0,9375.0,2820.0,19125.0,7776.0,4770.0,39375.0,19000.0,3920.0,10092.0,1170.0,9828.0,33250.0,3672.0,6109.0,4732.0,5192.0,2805.0,10000.0,4032.0,3922.0,25000.0,25500.0,13908.0,2173.0,5655.0,4500.0,8066.0,1440.0,5616.0,17500.0,870.0,3219.0,8036.0,1107.0,13833.0,2448.0,4956.0,1904.0,6288.0,12375.0,11250.0,13048.0,2184.0,14250.0,5504.0,26250.0,8125.0,15120.0,7805.0,1938.0,5372.0,15750.0,4067.0,24375.0,1088.0,2580.0,2016.0,27000.0,28750.0,13440.0,6440.0,3780.0,7320.0,624.0,17875.0,35750.0,6496.0,4350.0,29250.0,29700.0,12500.0,5880.0,1410.0,16625.0,26125.0,1406.0,8250.0,3675.0,22500.0,2250.0,1092.0,1242.0,9288.0,9943.0,12220.0,3237.0,7500.0,6878.0,9823.0,7178.0,1739.0,5160.0,45500.0,7050.0,5456.0,9900.0,23000.0,17500.0,7205.0,3500.0,5625.0,10304.0,5661.0,13500.0,7830.0,21125.0,9000.0,16500.0,4200.0,36000.0,18750.0,7040.0,20625.0,1414.0,8784.0,32000.0,15750.0,14739.0,4500.0,42250.0,2856.0,36000.0,1710.0,16320.0,19500.0,2997.0,19125.0,4633.0,3872.0,4756.0,3600.0,13500.0,8000.0,6909.0,26125.0,4056.0,8555.0,1216.0,5450.0,1392.0,17875.0,4033.0,15252.0,46875.0,14455.0,14000.0,6318.0,13000.0,1665.0,20250.0,1064.0,4797.0,25000.0,4028.0,19500.0,6000.0,1836.0,1716.0,2788.0,5181.0,41250.0,4992.0,2145.0,1377.0,8280.0,8820.0,3225.0,5014.0,4515.0,17875.0,39000.0,3450.0,3021.0,4212.0,18352.0,6006.0,6125.0,10336.0,4290.0,8575.0,40250.0,10000.0,6125.0,21000.0,15750.0,10875.0,32800.0,7875.0,24750.0,13090.0,7416.0,11070.0,31500.0,27000.0,7400.0,9000.0,6426.0,1428.0,18676.0,7350.0,26250.0,5661.0,2079.0,9000.0,12288.0,5670.0,19250.0,20250.0,5278.0,7353.0,1368.0,3922.0,21250.0,2520.0,45000.0,13392.0,6264.0,8960.0,26250.0,3444.0,1173.0,4200.0,30000.0,19520.0,4692.0,4147.0,32500.0,6681.0,1722.0,1410.0,24375.0,11583.0,5000.0,16000.0,4400.0,6000.0,5796.0,5928.0,23000.0,42000.0,6732.0,1131.0,3458.0,2538.0,18750.0,26000.0,12900.0,4500.0,2592.0,11375.0,1672.0,8836.0,31250.0,5037.0,3136.0,13157.0,4233.0,5418.0,17000.0,6086.0,13500.0,7964.0,8750.0,10920.0,5225.0,21000.0,7616.0,7021.0,3472.0,18375.0,11340.0,19250.0,1700.0,6250.0,3599.0,6468.0,1498.0,15052.0,10368.0,5764.0,12500.0,6006.0,8000.0,10230.0,17836.0,13000.0,44000.0,10000.0,18000.0,5782.0,30875.0,11880.0,5586.0,1914.0,3834.0,10000.0,882.0,2520.0,7200.0,12480.0,16875.0,8120.0,3696.0,13750.0,5100.0,30000.0,12375.0,20625.0,1045.0,12285.0,6018.0,5625.0,5000.0,3536.0,33000.0,16254.0,4664.0,33750.0,43500.0,20250.0,13000.0,23375.0,17250.0,13050.0,11792.0,8836.0,2318.0,22800.0,3002.0,33750.0,12500.0,984.0,4080.0,5750.0,11000.0,27000.0,7720.0,28125.0,13750.0,4425.0,11250.0,3780.0,16500.0,40625.0,3237.0,2162.0,5546.0,16500.0,2156.0,7250.0,7056.0,1786.0,15625.0,16250.0,6125.0,6048.0,4640.0,4205.0,6669.0,7350.0,7006.0,8832.0,1976.0,7980.0,1786.0,36000.0,21125.0,11662.0,2835.0,1656.0,48750.0,6930.0,6612.0,7482.0,10585.0,10353.0,2750.0,1508.0,12200.0,25875.0,1495.0,11875.0,6762.0,12000.0,2890.0,4018.0,12780.0,29000.0,28000.0,5060.0,1411.0,16660.0,4292.0,2560.0,6667.0,4450.0,33750.0,12090.0,7875.0,15376.0,22500.0,1419.0,15575.0,9625.0,2108.0,9000.0,2886.0,13900.0,3264.0,4125.0,10500.0,12375.0,8250.0,9633.0,13000.0,9000.0,4625.0,16000.0,21000.0,5500.0,1596.0,7560.0,9328.0,4292.0,5400.0,17500.0,16250.0,17500.0,5876.0,9288.0,19250.0,5568.0,10000.0,26000.0,11271.0,27500.0,9768.0,10000.0,20250.0,7168.0,6574.0,11999.0,10125.0,2090.0,35750.0,2964.0,5109.0,5100.0,4770.0,7020.0,26250.0,8816.0,4130.0,6314.0,47500.0,6027.0,9000.0,2850.0,12544.0,3000.0,4836.0,3072.0,3157.0,8400.0,25500.0,11977.0,19096.0,2352.0,28000.0,8844.0,16625.0,8700.0,45000.0,252.0,3038.0,931.0,14000.0,8512.0,9024.0,8000.0,8736.0,2442.0,2279.0,3510.0,3381.0,7488.0,4048.0,6291.0,43875.0,5376.0,6125.0,7344.0,10952.0,18375.0,8843.0,10290.0,4608.0,1632.0,8750.0,3816.0,1891.0,45000.0,13122.0,12006.0,2516.0,19125.0,43875.0,6624.0,4410.0,19500.0,21930.0,7622.0,10152.0,3344.0,14550.0,43875.0,8170.0,18000.0,28200.0,224.0,4459.0,10516.0,4399.0,42000.0,2340.0,2550.0,15750.0,5313.0,5568.0,7000.0,11362.0,12000.0,2304.0,3872.0,1156.0,6351.0,4687.0,19250.0,2184.0,7992.0,20196.0,19500.0,9468.0,36750.0,9400.0,29000.0,5312.0,11040.0,42625.0,3002.0,9000.0,18750.0,13500.0,14625.0,1722.0,1840.0,1650.0,6200.0,4144.0,13050.0,37500.0,9460.0,11375.0,27000.0,7000.0,31500.0,4060.0,7650.0,1728.0,3185.0,12375.0,10000.0,840.0,28875.0,4068.0,17875.0,8000.0,2156.0,38500.0,9750.0,27225.0,9867.0,26125.0,30875.0,18531.0,4200.0,34875.0,23450.0,11375.0,22000.0,2436.0,5551.0,4116.0,4370.0,8750.0,2071.0,7000.0,13125.0,2668.0,4736.0,33000.0,29250.0,4565.0,10074.0,22500.0,16000.0,9471.0,2583.0,3600.0,11250.0,24500.0,42525.0,7000.0,3248.0,2204.0,4004.0,10281.0,2208.0,5412.0,5120.0,10000.0,3675.0,23625.0,30250.0,2450.0,2552.0,7000.0,15750.0,3445.0,1612.0,6120.0,13500.0,12600.0,13750.0,1750.0,8550.0,8060.0,6647.0,3485.0,5000.0,880.0,1599.0,8750.0,12040.0,4680.0,3051.0,22000.0,11280.0,4465.0,15912.0,5800.0,30000.0,4080.0,1056.0,10269.0,16875.0,6902.0,9120.0,3920.0,4522.0,14070.0,4136.0,9000.0,1326.0,2622.0,4641.0,4564.0,4136.0,4536.0,7560.0,20250.0,14375.0,5148.0,8532.0,26250.0,6864.0,15000.0,26125.0,5800.0,1500.0,14000.0,15125.0,12546.0,14875.0,8096.0,2925.0,30000.0,24000.0,4104.0,10176.0,7600.0,5616.0,7728.0,992.0,3724.0,7467.0,13750.0,5250.0,1750.0,2666.0,7560.0,11250.0,7536.0,4872.0,19500.0,9744.0,6125.0,16250.0,3750.0,18800.0,2784.0,1365.0,31625.0,2726.0,19500.0,28750.0,5236.0,10976.0,5040.0,10625.0,8100.0,9430.0,17500.0,5248.0,13125.0,6200.0,4158.0,39000.0,8000.0,11628.0,1568.0,6298.0,9996.0,16500.0,7424.0,8750.0,10125.0,3920.0,2420.0,3500.0,12000.0,42625.0,10000.0,4320.0,5964.0,3828.0,13500.0,4118.0,3840.0,42000.0,34125.0,3973.0,4375.0,920.0,8750.0,26250.0,1121.0,5292.0,1653.0,4680.0,7208.0,3333.0,29700.0,40500.0,1827.0,33000.0,23625.0,5760.0,7171.0,2507.0,4752.0,2346.0,40250.0,25000.0,22500.0,20625.0,4692.0,1170.0,1326.0,6681.0,9984.0,18000.0,11250.0,10710.0,24000.0,6000.0,7676.0,35750.0,21000.0,3500.0,3504.0,10000.0,1950.0,3285.0,6623.0,31500.0,22425.0,2640.0,3500.0,11250.0,27625.0,26250.0,8050.0,2484.0,8976.0,560.0,3280.0,1593.0,2774.0,5655.0,3700.0,7875.0,5488.0,4368.0,4400.0,6084.0,8750.0,3979.0,5535.0,6300.0,5120.0,4788.0,31625.0,5168.0,15625.0,4995.0,3510.0,4294.0,3625.0,6664.0,5940.0,22000.0,7098.0,4161.0,3864.0,8778.0,2560.0,3720.0,7888.0,15750.0,18375.0,4680.0,7332.0,1872.0,8555.0,1118.0,25875.0,29750.0,45500.0,12644.0,3432.0,32500.0,3096.0,3535.0,4674.0,24375.0,5658.0,9000.0,16000.0,4716.0,17000.0,18000.0,5250.0,9472.0,1628.0,3696.0,4500.0,8700.0,10500.0,3666.0,24000.0,13936.0,25000.0,4096.0,1764.0,13230.0,3045.0,7474.0,7935.0,7865.0,27000.0,6664.0,1360.0,4725.0,16544.0,3735.0,12500.0,9500.0,12375.0,864.0,16625.0,1935.0,952.0,7000.0,1978.0,7260.0,40625.0,9205.0,6160.0,16500.0,2460.0,8100.0,14000.0,31500.0,2288.0,23800.0,6125.0,4859.0,12702.0,6012.0,7410.0,4704.0,6104.0,3740.0,4674.0,12375.0,2632.0,37375.0,9375.0,10224.0,10168.0,19500.0,9280.0,29750.0,11375.0,49500.0,4375.0,6324.0,1462.0,6441.0,4800.0,29250.0,4200.0,22500.0,19500.0,7875.0,37125.0,7452.0,21250.0,7003.0,4712.0,3876.0,8745.0,13824.0,6250.0,10976.0,5000.0,14375.0,8652.0,1820.0,10750.0,1672.0,3808.0,9030.0,35750.0,9000.0,38750.0,2900.0,6027.0,2684.0,20625.0,8823.0,6794.0,5500.0,3844.0,3887.0,7200.0,4312.0,2356.0,9434.0,14625.0,4360.0,12375.0,16500.0,4000.0,27000.0,16500.0,25375.0,5400.0,5456.0,25000.0,32000.0,21000.0,11375.0,3705.0,3212.0,7733.0,4995.0,7735.0,13750.0,42000.0,3741.0,6846.0,26000.0,2915.0,6902.0,2310.0,10988.0,6016.0,15517.0,20160.0,48000.0,3180.0,5800.0,12000.0,25000.0,3780.0,5434.0,6240.0,42625.0,7200.0,2350.0,7980.0,7100.0,11000.0,468.0,10619.0,48750.0,28000.0,8880.0,6930.0,7205.0,10659.0,10500.0,19250.0,2916.0,4760.0,1260.0,2021.0,21250.0,7740.0,13500.0,2268.0,3485.0,3150.0,864.0,39375.0,5000.0,14742.0,8000.0,7500.0,2760.0,16875.0,4312.0,6288.0,4992.0,26250.0,12750.0,1444.0,10382.0,2208.0,8260.0,12267.0,12040.0,4760.0,11875.0,1848.0,2673.0,2960.0,13160.0,43875.0,750.0,1050.0,2142.0,45375.0,2408.0,8700.0,48875.0,5568.0,12144.0,31000.0,3124.0,9971.0,7828.0,25875.0,2856.0,8745.0,28000.0,14000.0,2376.0,3672.0,6080.0,4125.0,4408.0,4524.0,46500.0,2639.0,31500.0,5000.0,3965.0,1862.0,3276.0,11800.0,4582.0,5625.0,3504.0,14625.0,10472.0,42250.0,5096.0,7640.0,22500.0,3078.0,12375.0,36000.0,2590.0,29250.0,10500.0,4312.0,3750.0,26250.0,2184.0,9672.0,7293.0,4134.0,9339.0,6210.0,9000.0,2584.0,21000.0,32000.0,22500.0,3536.0,43500.0,3600.0,2009.0,7920.0,21000.0,3750.0,26000.0,19250.0,1584.0,13688.0,3564.0,15360.0,2340.0,14875.0,5278.0,4221.0,8250.0,41250.0,22500.0,2576.0,37125.0,4095.0,6000.0,6750.0,6400.0,7344.0,17640.0,2310.0,1558.0,6174.0,24750.0,4825.0,4464.0,7140.0,11250.0,37250.0,20625.0,22500.0,7020.0,11250.0,12500.0,4290.0,1786.0,2068.0,11375.0,7772.0,12000.0,39000.0,33750.0,14000.0,7500.0,1872.0,17980.0,3888.0,1615.0,38250.0,21000.0,5644.0,5250.0,2107.0,2034.0,4023.0,2183.0,17875.0,23375.0,5734.0,24000.0,7290.0,13608.0,6000.0,1408.0,20250.0,9000.0,7840.0,320.0,4736.0,7500.0,7612.0,6750.0,2646.0,5096.0,2584.0,2940.0,11286.0,5814.0,7785.0,3864.0,13750.0,5372.0,12489.0,5538.0,1500.0,3034.0,9282.0,3000.0,10500.0,33750.0,27125.0,5103.0,29250.0,38000.0,5265.0,6123.0,3900.0,4002.0,8178.0,6875.0,6750.0,10600.0,2397.0,45000.0,7056.0,11500.0,1683.0,12054.0,8415.0,43500.0,5005.0,13000.0,24750.0,576.0,3478.0,4988.0,14322.0,12500.0,44000.0,7875.0,38250.0,8957.0,8060.0,16500.0,3375.0,6475.0,39875.0,3234.0,6578.0,8750.0,22500.0,38500.0,4032.0,2232.0,6072.0,238.0,10269.0,4343.0,7714.0,2400.0,10500.0,34375.0,3584.0,4212.0,2349.0,3034.0,4444.0,18375.0,10530.0,12921.0,7511.0,35625.0,8750.0,34500.0,13260.0,35750.0,27500.0,4000.0,5764.0,16625.0,10000.0,19430.0,4096.0,34500.0,3564.0,7938.0,5700.0,17094.0,7840.0,4988.0,41250.0,12544.0,28000.0,31500.0,19500.0,17880.0,14112.0,21000.0,2584.0,8008.0,5406.0,20000.0,6396.0,21000.0,36750.0,5500.0,13000.0,9384.0,9000.0,6258.0,7540.0,8364.0,15125.0,4375.0,7000.0,2805.0,4000.0,36000.0,38500.0,13750.0,19250.0,8697.0,19125.0,10500.0,11250.0,3332.0,1290.0,8241.0,19250.0,37500.0,2244.0,23625.0,5640.0,9000.0,4756.0,9108.0,10000.0,2550.0,40625.0,2656.0,1210.0,2646.0,7579.0,5040.0,6816.0,21375.0,12265.0,6656.0,2254.0,15000.0,7500.0,11564.0,18000.0,40500.0,2940.0,6919.0,47250.0,10692.0,3825.0,28750.0,3266.0,9984.0,14500.0,7462.0,12500.0,4935.0,9750.0,9984.0,6438.0,26000.0,10192.0,750.0,21000.0,4940.0,12250.0,30000.0,30000.0,43500.0,17875.0,6683.0,36250.0,6438.0,7500.0,23625.0,37375.0,16625.0,10325.0,12500.0,13500.0,3655.0,5868.0,19250.0,5280.0,10560.0,10904.0,507.0,16875.0,4320.0,8869.0,2196.0,2688.0,9282.0,5363.0,29250.0,10920.0,4389.0,2232.0,6419.0,19125.0,30875.0,22750.0,6125.0,3500.0,13500.0,5100.0,12500.0,33750.0,12804.0,24750.0,2288.0,11417.0,6750.0,5720.0,9750.0,4520.0,9000.0,5684.0,6528.0,4640.0,29750.0,4294.0,7130.0,11628.0,37125.0,8008.0,2112.0,9086.0,9553.0,5760.0,11055.0,6336.0,13750.0,9576.0,2632.0,15000.0,3230.0,6216.0,26250.0,10500.0,10584.0,6496.0,15000.0,16000.0,12705.0,5192.0,3375.0,4495.0,26125.0,6615.0,3731.0,8996.0,13860.0,12000.0,3264.0,36000.0,6384.0,15015.0,2464.0,2652.0,5481.0,8360.0,3125.0,3055.0,9000.0,10000.0,12375.0,12000.0,5700.0,11375.0,6210.0,6250.0,16500.0,2052.0,12000.0,11000.0,12852.0,24750.0,14000.0,40000.0,4410.0,12250.0,10125.0,8250.0,3248.0,4788.0,1275.0,27625.0,2196.0,6125.0,7875.0,1855.0,5625.0,42000.0,4375.0,1426.0,5390.0,2222.0,16200.0,3375.0,11200.0,2924.0,12250.0,2856.0,1496.0,11322.0,17875.0,6402.0,42500.0,5000.0,13750.0,11250.0,8844.0,31875.0,9375.0,25375.0,15125.0,6125.0,7100.0,34875.0,9039.0,2040.0,4389.0,4294.0,14446.0,7875.0,23400.0,4400.0,8190.0,7000.0,4675.0,1764.0,1599.0,20625.0,7875.0,1131.0,9454.0,7500.0,1610.0,8959.0,12250.0,34125.0,17000.0,16284.0,2704.0,16250.0,7314.0,1290.0,5559.0,3731.0,6612.0,2080.0,1496.0,3198.0,19125.0,30375.0,7500.0,10880.0,22000.0,1120.0,2652.0,11250.0,22000.0,36000.0,1260.0,2254.0,779.0,2700.0,2240.0,19000.0,21000.0,14875.0,12887.0,2240.0,2583.0,4662.0,7622.0,14742.0,3920.0,9086.0,800.0,17000.0,4128.0,5096.0,35750.0,1863.0,1804.0,22000.0,3864.0,45375.0,8918.0,1804.0,25500.0,3312.0,21375.0,16250.0,3120.0,2068.0,10224.0,9744.0,6080.0,11000.0,4532.0,2958.0,11750.0,4256.0,1856.0,18375.0,6000.0,6750.0,6900.0,5500.0,37375.0,735.0,2340.0,6517.0,10580.0,11470.0,5280.0,20025.0,8604.0,11250.0,8448.0,1104.0,25000.0,13000.0,7567.0,18000.0,22500.0,4320.0,15750.0,4150.0,16250.0,2583.0,12375.0,8316.0,42625.0,13845.0,5763.0,43500.0,12500.0,6324.0,6419.0,3182.0,4500.0,27500.0,5075.0,2800.0,15000.0,5371.0,9000.0,25875.0,11250.0,12000.0,4340.0,18000.0,8235.0,31500.0,996.0,40000.0,3132.0,8712.0,5265.0,9625.0,10388.0,15750.0,37050.0,10404.0,7943.0,34375.0,9061.0,11165.0,5535.0,42250.0,2457.0,19000.0,10974.0,18750.0,2250.0,27000.0,47250.0,2856.0,6875.0,6875.0,7612.0,20000.0,6020.0,3102.0,9400.0,11098.0,9000.0,30250.0,8250.0,5504.0,17875.0,14000.0,897.0,6409.0,2500.0,13500.0,11340.0,25375.0,2378.0,11480.0,5551.0,6000.0,1536.0,46750.0,20160.0,3912.0,3164.0,5031.0,2128.0,9660.0,6750.0,22500.0,1904.0,22750.0,2204.0,2145.0,22500.0,39375.0,21375.0,1974.0,9744.0,7208.0,2940.0,4416.0,8976.0,3360.0,24000.0,8750.0,2115.0,44000.0,9384.0,4324.0,13500.0,9688.0,11270.0,16500.0,6576.0,4192.0,4080.0,3384.0,22000.0,10125.0,10125.0,7680.0,15750.0,8750.0,13500.0,3872.0,4293.0,2080.0,5882.0,9625.0,2639.0,12240.0,4698.0,13416.0,2875.0,1900.0,4375.0,6250.0,8869.0,7566.0,14469.0,16250.0,24750.0,1395.0,8712.0,26250.0,6000.0,5250.0,10000.0,45375.0,4080.0,3375.0,3600.0,4340.0,1120.0,3723.0,4186.0,36250.0,0.0,8800.0,7009.0,476.0,33750.0,3636.0,3234.0,12500.0,8750.0,3852.0,2912.0,16500.0,2295.0,7421.0,6552.0,19500.0,21000.0,460.0,12500.0,13750.0,2352.0,2418.0,779.0,1050.0,344.0,9750.0,12040.0,22000.0,10125.0,8184.0,5626.0,3168.0,19125.0,14014.0,1666.0,3720.0,5439.0,2025.0,4046.0,4230.0,10000.0,1911.0,5782.0,39000.0,6419.0,11875.0,4800.0,31625.0,18000.0,4725.0,13500.0,31500.0,10626.0,6480.0,19250.0,10500.0,3312.0,30000.0,729.0,35750.0,9240.0,2565.0,4884.0,4350.0,2415.0,9300.0,23400.0,1971.0,7824.0,1368.0,48750.0,26250.0,14000.0,1972.0,3451.0,7384.0,24750.0,9009.0,10962.0,32500.0,4032.0,5750.0,15000.0,13797.0,4125.0,5880.0,6875.0,2320.0,1001.0,16500.0,12036.0,11938.0,3078.0,4826.0,5454.0,1650.0,4998.0,7205.0,31875.0,2535.0,1036.0,10500.0,2914.0,28000.0,40500.0,4278.0,6838.0,4836.0,7392.0,10846.0,12250.0,18000.0,2775.0,3500.0,5658.0,9882.0,2574.0,20625.0,4375.0,15750.0,2337.0,1904.0,46750.0,48750.0,37500.0,8990.0,3922.0,30000.0,1495.0,2990.0,11000.0,6750.0,8470.0,12375.0,4779.0,19000.0,31350.0,3861.0,9576.0,20625.0,4068.0,3705.0,10710.0,4982.0,40500.0,13398.0,5250.0,3978.0,12250.0,37125.0,7150.0,5488.0,24000.0,7788.0,4389.0,5796.0,1428.0,10143.0,12390.0,7760.0,23625.0,12000.0,18750.0,13858.0,24750.0,20000.0,406.0,7875.0,1056.0,5670.0,952.0,3774.0,23800.0,10320.0,24750.0,6500.0,2980.0,1496.0,2014.0,6579.0,12000.0,3000.0,14875.0,3552.0,3604.0,3500.0,2376.0,12825.0,6699.0,21000.0,4440.0,3663.0,4460.0,8190.0,9450.0,2050.0,22500.0,5775.0,29250.0,1050.0,5880.0,5916.0,21000.0,9625.0,10125.0,14875.0,10011.0,6232.0,1932.0,1680.0,750.0,31500.0,4611.0,4080.0,5406.0,8092.0,20625.0,6380.0,6120.0,4505.0,3360.0,5625.0,1848.0,3243.0,10449.0,7047.0,7000.0,8750.0,4953.0,1620.0,9581.0,6958.0,22000.0,35700.0,2448.0,2516.0,5800.0,13750.0,12642.0,2185.0,2058.0,2254.0,7680.0,4104.0,1215.0,874.0,5439.0,6952.0,10125.0,1232.0,11990.0,6846.0,12012.0,8500.0,2862.0,19500.0,22000.0,8092.0,8533.0,38000.0,3740.0,30000.0,5000.0,5904.0,12375.0,17500.0,4998.0,3120.0,19250.0,22500.0,1863.0,3250.0,1919.0,6422.0,26250.0,47125.0,7875.0,3348.0,5580.0,2368.0,1860.0,27500.0,8250.0,14625.0,16625.0,8062.0,23625.0,1856.0,19250.0,3750.0,28875.0,10176.0,18000.0,20250.0,9828.0,4375.0,42000.0,14875.0,2448.0,12582.0,6258.0,10125.0,9468.0,48875.0,1680.0,11000.0,3125.0,6105.0,11220.0,9945.0,3108.0,6125.0,24750.0,5246.0,4920.0,4032.0,3510.0,10000.0,5742.0,10787.0,14625.0,3000.0,3052.0,3645.0,28500.0,5488.0,17940.0,20000.0,3520.0,11875.0,2448.0,3705.0,13000.0,13000.0,2500.0,18360.0,1908.0,8750.0,2599.0,10744.0,11872.0,26250.0,28000.0,10125.0,10500.0,4500.0,6784.0,5617.0,3498.0,37125.0,14465.0,7729.0,6570.0,2784.0,1764.0,17664.0,40500.0,2990.0,18942.0,2772.0,4346.0,6815.0,1848.0,16000.0,4750.0,16500.0,5252.0,19500.0,8550.0,25875.0,19250.0,6656.0,5250.0,15750.0,37375.0,2507.0,6840.0,3885.0,2992.0,7000.0,28500.0,1368.0,37500.0,7080.0,39150.0,12500.0,750.0,31625.0,5304.0,5040.0,2030.0,5632.0,6240.0,8619.0,12896.0,14250.0,16443.0,7740.0,3840.0,13283.0,1653.0,2160.0,12375.0,1927.0,15000.0,6960.0,4096.0,4859.0,3876.0,3564.0,10152.0,5487.0,15750.0,4375.0,4900.0,10500.0,9828.0,22000.0,19500.0,18240.0,39000.0,6600.0,10208.0,42625.0,3380.0,22750.0,7006.0,11736.0,9016.0,3256.0,1444.0,3094.0,32000.0,8112.0,42750.0,2726.0,6460.0,45500.0,24000.0,11000.0,4674.0,2989.0,6696.0,7014.0,7101.0,1386.0,10125.0,6102.0,6090.0,6438.0,1924.0,24750.0,1968.0,20125.0,10626.0,8990.0,18000.0,6293.0,4664.0,8645.0,3995.0,6210.0,9800.0,1617.0,3333.0,16616.0,5537.0,10000.0,2688.0,2180.0,3444.0,1260.0,8250.0,3808.0,10416.0,9537.0,21750.0,14000.0,4256.0,5000.0,1500.0,7644.0,17500.0,7560.0,7038.0,15862.0,15244.0,3600.0,5922.0,4375.0,12500.0,37125.0,16625.0,10257.0,1408.0,48750.0,4992.0,10000.0,2560.0,11130.0,35625.0,7124.0,17500.0,11968.0,3234.0,10764.0,21375.0,6307.0,37375.0,2754.0,11650.0,528.0,7056.0,13750.0,19125.0,46500.0,6439.0,6154.0,1666.0,16348.0,4851.0,24750.0,1716.0,12500.0,5625.0,13500.0,15000.0,13192.0,4828.0,9000.0,4000.0,12000.0,16104.0,3219.0,35000.0,406.0,2968.0,5408.0,2400.0,21000.0,7612.0,4181.0,3360.0,8820.0,16256.0,3569.0,2457.0,2160.0,7320.0,9450.0,16625.0,2204.0,2464.0,7875.0,3168.0,4756.0,2754.0,2250.0,23250.0,4284.0,6324.0,882.0,2340.0,5688.0,17875.0,10500.0,6028.0,3648.0,13750.0,15000.0,21125.0,17875.0,10192.0,3465.0,7168.0,30000.0,14580.0,23250.0,2442.0,7458.0,6900.0,31625.0,2500.0,2240.0,3813.0,7595.0,5625.0,11375.0,3120.0,2788.0,13750.0,8700.0,6075.0,3135.0,12000.0,1450.0,3840.0,7003.0,13125.0,28875.0,19250.0,1014.0,5957.0,10944.0,6580.0,4700.0,18000.0,1792.0,11070.0,17250.0,4692.0,22000.0,14580.0,2030.0,3360.0,26125.0,2448.0,15500.0,2331.0,17110.0,2774.0,2438.0,5800.0,5244.0,4930.0,4410.0,1500.0,2618.0,13000.0,31500.0,10000.0,15125.0,1537.0,21000.0,40625.0,8064.0,7791.0,4524.0,4176.0,3567.0,9009.0,9945.0,12750.0,10125.0,4680.0,9100.0,5434.0,1363.0,920.0,20625.0,32500.0,37500.0,6785.0,27625.0,4375.0,3712.0,2250.0,37500.0,13000.0,10000.0,3888.0,13750.0,4420.0,35000.0,21750.0,2312.0,28000.0,1736.0,1677.0,6210.0,5513.0,8528.0,2912.0,1909.0,13676.0,6384.0,5412.0,11760.0,2912.0,9184.0,23375.0,14625.0,4000.0,4290.0,1066.0,2340.0,6474.0,12350.0,45000.0,6880.0,1488.0,1400.0,9300.0,3500.0,19500.0,6016.0,4200.0,7560.0,7599.0,7830.0,4514.0,6318.0,5280.0,7800.0,2128.0,9568.0,1488.0,29250.0,2184.0,22000.0,10584.0,700.0,23220.0,5625.0,8236.0,10000.0,21000.0,6900.0,2523.0,14000.0,13500.0,30000.0,35000.0,32625.0,2142.0,17500.0,9593.0,36000.0,28000.0,8294.0,1406.0,6244.0,7708.0,7192.0,10125.0,9947.0,34375.0,2640.0,15660.0,7920.0,23000.0,5040.0,20000.0,17000.0,17500.0,12804.0,4900.0,3294.0,7308.0,25000.0,2886.0,22000.0,6750.0,5265.0,19125.0,5311.0,3580.0,6608.0,7936.0,33250.0,42500.0,33000.0,38500.0,28750.0,28125.0,11250.0,23375.0,11250.0,1564.0,19250.0,26000.0,3852.0,6820.0,6844.0,9750.0,10100.0,5831.0,4515.0,32500.0,4352.0,14625.0,7332.0,16250.0,1425.0,990.0,20250.0,7665.0,3078.0,33000.0,6968.0,3468.0,1560.0,10560.0,46750.0,3780.0,1679.0,1125.0,12375.0,19250.0,10150.0,3965.0,11000.0,4914.0,11151.0,1664.0,2584.0,5250.0,6006.0,4125.0,37375.0,4352.0,369.0,1452.0,7735.0,31250.0,4370.0,6750.0,30250.0,9842.0,1430.0,20250.0,4290.0,4160.0,2250.0,5625.0,22500.0,8500.0,36000.0,7198.0,4500.0,10736.0,2625.0,5868.0,7335.0,4860.0,5580.0,2376.0,4182.0,10000.0,19125.0,10000.0,2640.0,8046.0,6250.0,7436.0,6048.0,7308.0,780.0,7755.0,24750.0,3100.0,31875.0,8092.0,4212.0,5248.0,22500.0,22500.0,4050.0,23375.0,10500.0,3854.0,3196.0,3696.0,18000.0,12250.0,6847.0,5310.0,1054.0,2600.0,33750.0,3024.0,21000.0,8000.0,1200.0,11375.0,5000.0,8418.0,2448.0,6292.0,16250.0,6120.0,20000.0,11648.0,14625.0,19200.0,9264.0,12000.0,5320.0,42625.0,4982.0,6992.0,8410.0,16170.0,21000.0,22500.0,12349.0,3725.0,12500.0,17500.0,10000.0,8874.0,2040.0,8250.0,1140.0,6162.0,3000.0,2576.0,3552.0,36000.0,7440.0,8272.0,6450.0,10125.0,9417.0,13500.0,2880.0,8096.0,10500.0,41250.0,1368.0,12000.0,13158.0,35000.0,24750.0,1872.0,25875.0,7093.0,19500.0,8028.0,4095.0,9048.0,18000.0,5746.0,13940.0,5628.0,4932.0,23375.0,1512.0,8265.0,33000.0,42250.0,8550.0,3030.0,3255.0,31625.0,3696.0,4590.0,8750.0,3936.0,5536.0,1792.0,7344.0,14151.0,7875.0,7680.0,5989.0,3818.0,3591.0,15000.0,7238.0,28750.0,20625.0,16240.0,2394.0,4508.0,1722.0,40625.0,6272.0,8000.0,4004.0,13000.0,11250.0,38250.0,7050.0,7332.0,3100.0,13750.0,18000.0,17500.0,4266.0,10560.0,2590.0,8745.0,3584.0,8000.0,16500.0,23625.0,4216.0,4480.0,3312.0,5472.0,644.0,10912.0,28750.0,6407.0,5000.0,2646.0,3450.0,4554.0,1110.0,8500.0,2280.0,6669.0,11844.0,3708.0,13041.0,3131.0,7560.0,5512.0,1887.0,6272.0,26125.0,2584.0,8505.0,5250.0,16500.0,8000.0,8500.0,3927.0,4698.0,5850.0,14625.0,10199.0,6204.0,8556.0,46500.0,30000.0,8000.0,1862.0,12500.0,8918.0,16320.0,2760.0,3186.0,7020.0,7616.0,10127.0,11000.0,22500.0,6120.0,5208.0,10500.0,11700.0,4836.0,25000.0,6000.0,8250.0,2808.0,2112.0,4375.0,14250.0,2890.0,15616.0,6624.0,3807.0,26250.0,4978.0,6902.0,12250.0,6600.0,6174.0,893.0,42625.0,2160.0,15478.0,11286.0,7128.0,30250.0,3427.0,6916.0,8484.0,31250.0,42625.0,42250.0,33750.0,203.0,9000.0,14586.0,15125.0,10500.0,203.0,15000.0,47250.0,1334.0,7616.0,19462.0,12064.0,3750.0,6762.0,5568.0,28125.0,37500.0,4982.0,2200.0,17955.0,6790.0,9044.0,8000.0,8000.0,4847.0,8502.0,4224.0,1768.0,5984.0,37500.0,2491.0,11500.0,3450.0,9024.0,39000.0,3225.0,1296.0,8806.0,2408.0,13500.0,3626.0,5250.0,3000.0,11997.0,10000.0,8850.0,11088.0,10000.0,5184.0,3822.0,8400.0,10230.0,5000.0,6194.0,2460.0,6750.0,5964.0,30000.0,26000.0,3332.0,6875.0,22000.0,4125.0,8288.0,3220.0,3275.0,884.0,6174.0,17500.0,17640.0,4914.0,5166.0,7876.0,11340.0,7500.0,13000.0,2700.0,3096.0,8066.0,6528.0,1452.0,6890.0,7029.0,1512.0,10500.0,45000.0,12375.0,9000.0,4620.0,28125.0,3577.0,11368.0,18000.0,5504.0,6664.0,19500.0,28000.0,4000.0,1740.0,20000.0,9490.0,11990.0,1840.0,11250.0,1664.0,7448.0,3570.0,48750.0,8251.0,7072.0,2064.0,24000.0,24000.0,4522.0,6228.0,22750.0,2200.0,3108.0,8316.0,39000.0,3570.0,20625.0,10353.0,35000.0,506.0,11648.0,6084.0,8308.0,29250.0,6264.0,40625.0,1064.0,2550.0,0.0,9075.0,7392.0,6000.0,28875.0,42000.0,7830.0,10528.0,37125.0,11368.0,14625.0,5824.0,1628.0,6000.0,24750.0,896.0,21000.0,5000.0,33250.0,47250.0,8662.0,8250.0,21000.0,2914.0,4719.0,1450.0,1848.0,31875.0,4446.0,4860.0,5616.0,7500.0,8856.0,7875.0,4092.0,8304.0,1364.0,1824.0,18821.0,1512.0,1696.0,3196.0,5684.0,4320.0,6084.0,2407.0,5270.0,2323.0,31500.0,6000.0,3354.0,5148.0,48875.0,5952.0,15000.0,15120.0,5782.0,4914.0,3473.0,6125.0,11151.0,19250.0,11086.0,11250.0,11011.0,4650.0,1989.0,8160.0,34125.0,11250.0,5151.0,16625.0,11925.0,4844.0,2625.0,11000.0,4320.0,2288.0,3920.0,3640.0,2093.0,4147.0,3403.0,9360.0,4500.0,4683.0,7728.0,5376.0,38750.0,2548.0,8400.0,4715.0,1512.0,3795.0,6705.0,12000.0,11250.0,16000.0,22750.0,1672.0,9000.0,10962.0,13500.0,2024.0,4375.0,1088.0,1026.0,11692.0,4050.0,1904.0,3125.0,25000.0,39000.0,4030.0,7084.0,6237.0,5625.0,6222.0,41250.0,37375.0,7161.0,16875.0,6860.0,6300.0,8640.0,9000.0,28125.0,12500.0,17500.0,2091.0,15575.0,3344.0,2067.0,4879.0,2730.0,31250.0,8694.0,11176.0,9625.0,15000.0,10246.0,38000.0,28125.0,12348.0,14875.0,19000.0,3488.0,5658.0,11977.0,6030.0,15000.0,26250.0,2088.0,2886.0,10500.0,6400.0,6916.0,3458.0,7680.0,10500.0,2331.0,10500.0,4356.0,10000.0,1976.0,4719.0,9843.0,1886.0,17940.0,13500.0,5148.0,12936.0,7216.0,2296.0,483.0,1070.0,42500.0,6468.0,5250.0,11592.0,8000.0,6120.0,11340.0,4004.0,2295.0,12500.0,34875.0,5976.0,3108.0,3750.0,2176.0,3402.0,1740.0,9600.0,3360.0,3900.0,3127.0,2160.0,2550.0,15000.0,7500.0,36000.0,13500.0,2405.0,6840.0,20000.0,2880.0,6156.0,8037.0,1276.0,5292.0,11000.0,1479.0,8723.0,3672.0,3276.0,4998.0,6210.0,23375.0,5304.0,2820.0,6560.0,8823.0,6875.0,25875.0,684.0,7672.0,44000.0,4896.0,1458.0,44000.0,42250.0,46500.0,5640.0,13500.0,5764.0,3500.0,7304.0,33250.0,4454.0,8823.0,2250.0,8250.0,8424.0,3276.0,6525.0,32500.0,31250.0,10000.0,12500.0,18000.0,21375.0,2176.0,3040.0,8640.0,3654.0,10500.0,10625.0,15696.0,40625.0,10860.0,8476.0,16250.0,13202.0,2484.0,29750.0,1008.0,26000.0,3075.0,11375.0,4636.0,9216.0,5904.0,1444.0,31500.0,5246.0,23375.0,31500.0,7021.0,2268.0,29750.0,18375.0,21375.0,9514.0,22750.0,24000.0,33000.0,16000.0,7788.0,1624.0,24750.0,8943.0,5280.0,28875.0,5440.0,9078.0,3904.0,4000.0,18000.0,2856.0,16625.0,3852.0,25000.0,14000.0,42000.0,26125.0,28875.0,22500.0,10985.0,8100.0,4350.0,7182.0,3016.0,10000.0,2800.0,3808.0,4940.0,11178.0,2929.0,3640.0,4700.0,6875.0,7500.0,22500.0,36750.0,3880.0,10207.0,4068.0,2318.0,8150.0,4641.0,17360.0,416.0,3451.0,19800.0,38500.0,5117.0,22500.0,1300.0,24000.0,1944.0,28000.0,1729.0,24750.0,5577.0,7875.0,875.0,26250.0,6500.0,15600.0,26125.0,7056.0,6750.0,30000.0,7140.0,35750.0,15750.0,3232.0,232.0,12000.0,3780.0,1947.0,12375.0,11875.0,6048.0,8478.0,10846.0,25000.0,2346.0,17875.0,6250.0,15125.0,18000.0,8750.0,624.0,2632.0,1125.0,4293.0,16800.0,351.0,4836.0,4500.0,3680.0,6208.0,14625.0,7728.0,6000.0,6650.0,6026.0,13500.0,10125.0,18538.0,3552.0,17500.0,4620.0,15000.0,754.0,7682.0,5365.0,3280.0,5978.0,13500.0,2279.0,8750.0,4592.0,10824.0,1593.0,13750.0,2682.0,15125.0,6815.0,5742.0,2925.0,11050.0,3654.0,3990.0,13500.0,27000.0,5376.0,13572.0,27500.0,6324.0,18000.0,11985.0,7500.0,7875.0,13500.0,12375.0,14625.0,1875.0,9009.0,9238.0,392.0,12350.0,13500.0,7500.0,12246.0,4375.0,2712.0,1680.0,4368.0,23625.0,10000.0,14500.0,14000.0,31500.0,6048.0,5764.0,9000.0,30000.0,7500.0,22750.0,2025.0,15125.0,3500.0,6536.0,6923.0,1764.0,6960.0,2494.0,3750.0,6944.0,13500.0,7644.0,5880.0,3125.0,4000.0,7875.0,6336.0,5454.0,32500.0,1334.0,6448.0,4401.0,32500.0,45500.0,24750.0,13500.0,0.0,5112.0,1254.0,10481.0,24000.0,2992.0,3848.0,11250.0,30000.0,14784.0,41250.0,28875.0,2952.0,5076.0,1512.0,4224.0,7500.0,13750.0,4092.0,11310.0,27500.0,2244.0,3750.0,5117.0,20250.0,3312.0,20125.0,19800.0,2205.0,6215.0,7000.0,12720.0,13056.0,7504.0,6318.0,8664.0,14850.0,7000.0,11375.0,8272.0,25000.0,2257.0,6157.0,37500.0,5250.0,5467.0,12375.0,4620.0,8046.0,13750.0,8262.0,31875.0,21125.0,1394.0,5460.0,14000.0,23625.0,5676.0,2296.0,2214.0,2574.0,17500.0,12000.0,7990.0,2880.0,19125.0,12000.0,2204.0,2158.0,7176.0,18000.0,1829.0,12540.0,2244.0,18000.0,2128.0,2349.0,27000.0,43875.0,4500.0,6125.0,7875.0,4212.0,25000.0,9504.0,1539.0,3741.0,2370.0,12540.0,11520.0,8125.0,9126.0,1540.0,4488.0,17500.0,6160.0,30000.0,7245.0,2548.0,5852.0,16500.0,1836.0,6086.0,14040.0,21000.0,12926.0,1176.0,7950.0,3828.0,9617.0,21250.0,12896.0,3808.0,28000.0,13536.0,1170.0,28750.0,9000.0,1368.0,15750.0,25500.0,1316.0,4060.0,2597.0,16875.0,2628.0,4410.0,1127.0,26250.0,1150.0,7875.0,29250.0,2457.0,5985.0,9758.0,5440.0,37375.0,12375.0,7359.0,5032.0,3432.0,9504.0,6875.0,1170.0,5265.0,1710.0,5625.0,8322.0,24000.0,18224.0,12276.0,21000.0,6272.0,24500.0,8296.0,6902.0,1500.0,5166.0,7316.0,3240.0,6345.0,7876.0,7038.0,5782.0,19250.0,31875.0,9375.0,7000.0,3363.0,5250.0,2091.0,7125.0,936.0,1125.0,7684.0,11340.0,16500.0,48125.0,9744.0,15750.0,16848.0,11250.0,9375.0,6321.0,5922.0,4488.0,5989.0,14144.0,17000.0,24500.0,2850.0,12516.0,10296.0,8000.0,25375.0,5661.0,38250.0,7875.0,6750.0,27600.0,7500.0,8788.0,25000.0,16500.0,11319.0,5250.0,1728.0,8096.0,3074.0,4248.0,5000.0,7500.0,25875.0,1271.0,15600.0,21000.0,4592.0,6272.0,10840.0,26000.0,27000.0,5625.0,3042.0,15950.0,24750.0,6150.0,1890.0,9750.0,8432.0,5117.0,3468.0,5358.0,6750.0,5544.0,9000.0,13000.0,3745.0,17250.0,40500.0,1127.0,29700.0,9870.0,377.0,11375.0,6084.0,13750.0,2728.0,15000.0,14490.0,1092.0,1500.0,2142.0,6875.0,12600.0,40625.0,2125.0,11736.0,20250.0,31625.0,17500.0,12600.0,9650.0,5632.0,1131.0,7520.0,45500.0,6468.0,8151.0,17600.0,11250.0,5720.0,11000.0,1848.0,11000.0,8500.0,1989.0,8880.0,38500.0,1885.0,9600.0,14094.0,12586.0,31500.0,7452.0,10488.0,2183.0,5376.0,13601.0,12375.0,9000.0,2464.0,13750.0,4500.0,4459.0,7500.0,11250.0,7920.0,6875.0,11832.0,10912.0,13048.0,1050.0,2312.0,33000.0,6000.0,5676.0,12500.0,28500.0,1710.0,13650.0,22000.0,28750.0,2464.0,16500.0,8190.0,5168.0,10125.0,12500.0,5184.0,4779.0,2214.0,3200.0,4000.0,6375.0,31500.0,4305.0,34375.0,5880.0,14000.0,28125.0,5632.0,7875.0,9625.0,8575.0,3944.0,6160.0,7200.0,11900.0,35000.0,2262.0,972.0,19000.0,6954.0,8250.0,26125.0,4968.0,6132.0,43000.0,7498.0,14000.0,18750.0,8125.0,6125.0,3375.0,4408.0,8000.0,4375.0,4176.0,12500.0,1416.0,11024.0,11250.0,10150.0,1254.0,10335.0,6369.0,4536.0,3000.0,14000.0,4237.0,5887.0,5328.0,1952.0,2418.0,3000.0,5100.0,27000.0,9744.0,2173.0,9180.0,2639.0,10152.0,6528.0,4375.0,32000.0,3869.0,38750.0,6000.0,18000.0,8304.0,6288.0,7938.0,38500.0,4092.0,24000.0,12375.0,3444.0,6250.0,21250.0,11169.0,15000.0,1568.0,43125.0,2277.0,19250.0,3584.0,3990.0,2040.0,3750.0,11895.0,7744.0,2993.0,16250.0,3213.0,39875.0,9000.0,18000.0,13260.0,4800.0,2592.0,24000.0,6528.0,3392.0,21250.0,6120.0,26250.0,33750.0,6250.0,18750.0,5763.0,0.0,8800.0,3485.0,1377.0,14625.0,21000.0,1232.0,39750.0,4620.0,2725.0,2346.0,1292.0,8000.0,585.0,3102.0,3927.0,8323.0,4620.0,13750.0,3536.0,6800.0,25000.0,3500.0,1950.0,42625.0,9625.0,10500.0,6250.0,5640.0,3828.0,9744.0,15000.0,8150.0,3200.0,26250.0,9625.0,9672.0,13500.0,18000.0,39000.0,15000.0,20625.0,7176.0,18000.0,45000.0,3822.0,4620.0,7074.0,3975.0,38750.0,3021.0,23625.0,7000.0,8729.0,33000.0,28000.0,8085.0,4495.0,22500.0,29750.0,4816.0,1521.0,9920.0,34500.0,9792.0,5400.0,224.0,2975.0,1666.0,13570.0,2976.0,7875.0,3657.0,3174.0,39000.0,17000.0,1170.0,5206.0,13756.0,2464.0,3626.0,1422.0,5278.0,6210.0,12000.0,2640.0,37125.0,12500.0,9625.0,160.0,10000.0,11092.0,720.0,28000.0,7560.0,5040.0,12375.0,10000.0,19000.0,5763.0,41250.0,4900.0,5600.0,6699.0,4060.0,5625.0,22000.0,34125.0,46250.0,6909.0,21250.0,27500.0,12480.0,7875.0,2700.0,3108.0,22500.0,4030.0,3060.0,13770.0,7600.0,4444.0,9440.0,7500.0,13750.0,1387.0,2000.0,10556.0,4823.0,1125.0,3705.0,16324.0,5024.0,1360.0,10850.0,6909.0,5250.0,15750.0,7661.0,11016.0,5510.0,15240.0,2250.0,47250.0,8281.0,9842.0,37125.0,3876.0,5292.0,4370.0,3328.0,8058.0,4000.0,1122.0,19500.0,10125.0,550.0,3712.0,2904.0,11250.0,33250.0,15125.0,17875.0,1960.0,5700.0,7000.0,35000.0,6000.0,25375.0,5198.0,8000.0,2100.0,3157.0,3120.0,2700.0,8400.0,5828.0,3060.0,2925.0,3750.0,12596.0,779.0,2632.0,11250.0,48000.0,31875.0,37500.0,1189.0,11564.0,7392.0,5950.0,6110.0,13770.0,10478.0,1452.0,4294.0,42000.0,7987.0,2065.0,1496.0,24000.0,3750.0,21125.0,6000.0,3575.0,4264.0,5500.0,6250.0,13750.0,5248.0,1885.0,7150.0,12000.0,12825.0,3959.0,11618.0,15000.0,9982.0,6670.0,10738.0,33000.0,1305.0,5278.0,6084.0,5850.0,13125.0,9604.0,18375.0,26000.0,11875.0,1980.0,6500.0,8000.0,14000.0,19720.0,19125.0,5916.0,27750.0,20000.0,36000.0,11280.0,13500.0,9880.0,11718.0,6794.0,13500.0,40500.0,4375.0,5365.0,8250.0,12500.0,2480.0,37375.0,7560.0,2992.0,4032.0,25875.0,20250.0,4418.0,9554.0,3488.0,11718.0,1292.0,6500.0,1740.0,5130.0,30000.0,4500.0,3780.0,7644.0,24750.0,9074.0,16500.0,13125.0,4224.0,41250.0,5612.0,15023.0,26250.0,15000.0,1440.0,16500.0,5100.0,8750.0,6785.0,30875.0,6149.0,5250.0,16875.0,13750.0,3348.0,4864.0,31000.0,4173.0,5750.0,7938.0,24000.0,11310.0,10416.0,3080.0,6510.0,27500.0,15750.0,4698.0,12250.0,42500.0,4368.0,7896.0,9617.0,2277.0,21000.0,2173.0,4224.0,29250.0,2668.0,10192.0,5066.0,17000.0,5115.0,3102.0,13110.0,3600.0,11000.0,7335.0,35000.0,6750.0,20625.0,2142.0,7000.0,17000.0,19000.0,3232.0,6969.0,2961.0,11110.0,2720.0,19250.0,4018.0,5936.0,3348.0,6486.0,2500.0,12150.0,16625.0,9646.0,4000.0,13184.0,6000.0,7011.0,10738.0,7616.0,31500.0,4000.0,19250.0,40500.0,1610.0,3375.0,5180.0,1875.0,2744.0,3850.0,16225.0,21125.0,3008.0,9982.0,8400.0,3924.0,19250.0,897.0,4551.0,13650.0,5642.0,7875.0,280.0,18000.0,9604.0,3375.0,2652.0,37500.0,2116.0,4736.0,18000.0,18000.0,37500.0,9152.0,13056.0,792.0,30375.0,4200.0,1269.0,5865.0,46750.0,6372.0,12064.0,23100.0,40500.0,777.0,11985.0,25500.0,5625.0,10125.0,49500.0,7000.0,16500.0,1357.0,4000.0,46500.0,3276.0,17500.0,256.0,3071.0,4118.0,11596.0,9750.0,15000.0,3808.0,13664.0,7059.0,1638.0,7392.0,4847.0,4370.0,3059.0,4500.0,8990.0,1932.0,5720.0,28500.0,8250.0,3225.0,940.0,20000.0,2622.0,5814.0,1587.0,3984.0,11250.0,33750.0,12250.0,11375.0,11662.0,10500.0,4335.0,1162.0,19250.0,4323.0,20000.0,3451.0,39875.0,1326.0,13000.0,9828.0,19500.0,7100.0,12000.0,4050.0,2880.0,23000.0,2576.0,1568.0,2600.0,8370.0,8188.0,8364.0,7000.0,19125.0,4482.0,9853.0,6272.0,26000.0,12000.0,4340.0,7714.0,4224.0,3100.0,38250.0,10500.0,28875.0,25875.0,8000.0,10900.0,7626.0,35775.0,5248.0,11000.0,7888.0,2067.0,22000.0,3333.0,29250.0,2628.0,20125.0,7826.0,1350.0,7840.0,45000.0,11250.0,825.0,3256.0,14000.0,21375.0,46875.0,4150.0,15750.0,32500.0,4344.0,6336.0,6875.0,3200.0,20000.0,11000.0,30375.0,23750.0,7875.0,6080.0,5292.0,2891.0,531.0,2784.0,17500.0,12474.0,30250.0,21250.0,26250.0,2091.0,24750.0,19720.0,14000.0,19500.0,10000.0,16625.0,8740.0,3186.0,17066.0,3648.0,7000.0,11088.0,26000.0,4500.0,3712.0,8000.0,6000.0,7700.0,1696.0,5928.0,7695.0,4242.0,3888.0,3584.0,2250.0,28875.0,4375.0,16650.0,32500.0,40500.0,6302.0,4500.0,7254.0,18125.0,1647.0,36000.0,7000.0,10125.0,2886.0,5208.0,2400.0,16875.0,3960.0,3003.0,27000.0,23625.0,4715.0,19125.0,5460.0,8568.0,5244.0,9632.0,1254.0,2964.0,3936.0,3078.0,6450.0,2482.0,10192.0,7917.0,6500.0,24750.0,4658.0,6664.0,5252.0,6174.0,23375.0,7242.0,1272.0,8000.0,5880.0,2500.0,1938.0,7714.0,20212.0,13125.0,7056.0,10065.0,24000.0,3157.0,8401.0,4687.0,2176.0,259.0,5750.0,8820.0,2618.0],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Sales\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"count\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Distribution of sales\"},\"barmode\":\"relative\"},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('dac88c3e-1c78-4605-a622-e46bc82d78a8');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "halfdata = halfdata.sample(frac=1).reset_index(drop=True)"
      ],
      "metadata": {
        "id": "j4vIcsNyTZax"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "c12d220c50e14cc4be89e3e94a41736f",
        "deepnote_cell_type": "text-cell-h1",
        "id": "0XR_5OLgId6_"
      },
      "source": [
        "# Линейная Регрессия для данных без хвоста"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "b59f7922",
        "execution_start": 1708811852318,
        "execution_millis": 251,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "09d5696792814362b6746c7b2bd10eb7",
        "deepnote_cell_type": "code",
        "id": "D6NOlWMCId7A"
      },
      "source": [
        "Xl= halfdata.values[:,(0,1,2,3,4,5,6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)]\n",
        "Yl= halfdata.values[:, 21]\n",
        "\n",
        "\n",
        "Xl_train, Xl_test, yl_train, yl_test = train_test_split(Xl, Yl, test_size=0.15, random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "a21255bd",
        "execution_start": 1708811852318,
        "execution_millis": 252,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "1a10f66c7f81455998b86f13b781f9d3",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yN8QzqoVId7A",
        "outputId": "387c5ff9-1ef1-4ced-fb01-705d955dd075"
      },
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(Xl_train,yl_train)\n",
        "yl_pred = lr.predict(Xl_test)\n",
        "r2=r2_score(yl_test, yl_pred)\n",
        "print(r2)\n",
        "\n",
        "# Рассчет MSE\n",
        "mse = np.mean((yl_test - yl_pred)**2)\n",
        "\n",
        "# Рассчет MAPE\n",
        "mape = np.mean(np.abs((yl_test - yl_pred)) / (yl_test)) * 100\n",
        "\n",
        "print(\"MSE:\", mse)\n",
        "print(\"MAPE:\", mape)\n",
        "#80.82754325397129"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.4635679588405671\n",
            "MSE: 58969841.68820824\n",
            "MAPE: inf\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-430-b33b4366fdc1>:11: RuntimeWarning:\n",
            "\n",
            "divide by zero encountered in divide\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "3732649d4155408ca96f37c80917b827",
        "deepnote_cell_type": "text-cell-h1",
        "id": "c-3BwcNjId7A"
      },
      "source": [
        "# CATBOOST для данных без хвоста"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "8cbe1a02",
        "execution_start": 1708811852419,
        "execution_millis": 386,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "db298c9669ef483da802bb2e0de64c96",
        "deepnote_cell_type": "code",
        "id": "UjZ-9qCnId7A"
      },
      "source": [
        "Xc= halfdata.values[:,(0,1,2,3,4,5,6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)]\n",
        "Yc= halfdata.values[:, 21]\n",
        "\n",
        "\n",
        "Xc_train, Xc_test, yc_train, yc_test = train_test_split(Xc, Yc, test_size=0.15, random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "57c13db3",
        "execution_start": 1708811852452,
        "execution_millis": 7230,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "4b73746eeb7d4efdaf068890754896c1",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kD7L4IeyId7A",
        "outputId": "5832c419-d5b0-4946-929b-c5e955a12548"
      },
      "source": [
        "from catboost import Pool, CatBoostRegressor\n",
        "\n",
        "# initialize Pool\n",
        "train_pool = Pool(Xc_train, yc_train)\n",
        "test_pool = Pool(Xc_test, yc_test)\n",
        "\n",
        "# specify the training parameters\n",
        "model = CatBoostRegressor(iterations=2500, loss_function='MAE', verbose=100)\n",
        "#train the model\n",
        "model.fit(train_pool)\n",
        "# make the prediction using the resulting model\n",
        "predc = model.predict(test_pool)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0:\tlearn: 7217.8509083\ttotal: 2.03ms\tremaining: 5.08s\n",
            "100:\tlearn: 4423.5585925\ttotal: 178ms\tremaining: 4.23s\n",
            "200:\tlearn: 4082.8547235\ttotal: 356ms\tremaining: 4.07s\n",
            "300:\tlearn: 3860.8444478\ttotal: 535ms\tremaining: 3.9s\n",
            "400:\tlearn: 3655.0703607\ttotal: 724ms\tremaining: 3.79s\n",
            "500:\tlearn: 3509.7467657\ttotal: 905ms\tremaining: 3.61s\n",
            "600:\tlearn: 3393.9336038\ttotal: 1.08s\tremaining: 3.41s\n",
            "700:\tlearn: 3297.6005674\ttotal: 1.25s\tremaining: 3.22s\n",
            "800:\tlearn: 3227.4470739\ttotal: 1.43s\tremaining: 3.03s\n",
            "900:\tlearn: 3169.8730396\ttotal: 1.6s\tremaining: 2.84s\n",
            "1000:\tlearn: 3123.2261784\ttotal: 1.79s\tremaining: 2.67s\n",
            "1100:\tlearn: 3076.0619951\ttotal: 1.96s\tremaining: 2.49s\n",
            "1200:\tlearn: 3029.9041793\ttotal: 2.13s\tremaining: 2.3s\n",
            "1300:\tlearn: 2996.0407237\ttotal: 2.3s\tremaining: 2.12s\n",
            "1400:\tlearn: 2967.9199138\ttotal: 2.47s\tremaining: 1.94s\n",
            "1500:\tlearn: 2933.6283275\ttotal: 2.65s\tremaining: 1.76s\n",
            "1600:\tlearn: 2906.1807632\ttotal: 2.83s\tremaining: 1.59s\n",
            "1700:\tlearn: 2882.0464036\ttotal: 3s\tremaining: 1.41s\n",
            "1800:\tlearn: 2858.8326465\ttotal: 3.18s\tremaining: 1.23s\n",
            "1900:\tlearn: 2839.8825611\ttotal: 3.35s\tremaining: 1.06s\n",
            "2000:\tlearn: 2819.6211820\ttotal: 3.52s\tremaining: 878ms\n",
            "2100:\tlearn: 2799.3698087\ttotal: 3.71s\tremaining: 704ms\n",
            "2200:\tlearn: 2782.4014389\ttotal: 3.88s\tremaining: 527ms\n",
            "2300:\tlearn: 2766.8388854\ttotal: 4.05s\tremaining: 351ms\n",
            "2400:\tlearn: 2752.6337840\ttotal: 4.23s\tremaining: 175ms\n",
            "2499:\tlearn: 2740.3205789\ttotal: 4.41s\tremaining: 0us\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "9de55dbe",
        "execution_start": 1708811859689,
        "execution_millis": 129,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "ef932d58d0214912b55883e3ad2cfa94",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1VvLYx_IId7A",
        "outputId": "95316798-1b4f-4687-95c2-b5c27ee3b5d2"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "\n",
        "rmse = mean_squared_error(yc_test, predc, squared=False)\n",
        "mae = mean_absolute_error(yc_test, predc)\n",
        "# Рассчет MAPE\n",
        "mape = np.mean((np.abs(yc_test - predc)) / (yc_test)) * 100\n",
        "\n",
        "print(\"MAE:\", mae)\n",
        "print(\"MAPE:\", mape)\n",
        "print(\"RMSE:\", rmse)\n",
        "#35.79969211069319\n",
        "#34.65593904743338\n",
        "#36.79607730978182"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 3186.0251982682225\n",
            "MAPE: inf\n",
            "RMSE: 5460.195360888069\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-433-4487eb18e922>:6: RuntimeWarning:\n",
            "\n",
            "divide by zero encountered in divide\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "eb1ccba2",
        "execution_start": 1708811859694,
        "execution_millis": 331,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "449ab2a266a94427bccb003880f486da",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H01SnZEVId7A",
        "outputId": "ca6a675c-1562-4edf-f468-d7222dedcc6b"
      },
      "source": [
        "bias = calculate_bias(predc, yc_test)\n",
        "print(\"BIAS данных:\", bias)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BIAS данных: -456.25917441303045\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "49452f2b",
        "execution_start": 1708811859702,
        "execution_millis": 1052,
        "deepnote_to_be_reexecuted": false,
        "deepnote_output_height_limit_disabled": true,
        "cell_id": "aa6aec67896749f5ab0b1e6f047a9741",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "id": "bHmfZ3QFId7B",
        "outputId": "6fa464d2-4360-46c6-afde-f26a0b766fee"
      },
      "source": [
        "sk = pd.Series(model.feature_importances_,(df2.drop(\"Total Sales\", axis=1)).columns)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(16,14))\n",
        "sk.plot.bar(ax=ax, color=\"#FB8500\")\n",
        "ax.set_title(\"Важность признаков\")\n",
        "ax.set_ylabel('Важность')\n",
        "fig.tight_layout()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "e3330001",
        "execution_start": 1708811860771,
        "execution_millis": 2691,
        "deepnote_to_be_reexecuted": false,
        "cell_id": "e48c3629379543f58f79d1d971240e4e",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "n9yGGipCId7B",
        "outputId": "e7aa39da-6863-43fb-ed98-a44a52da57ec"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.lines as mlines\n",
        "import matplotlib.transforms as mtransforms\n",
        "\n",
        "x, y = np.random.random((2, 100))*2\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(predc,yc_test)\n",
        "line = mlines.Line2D([0, 1], [0, 1], color='red')\n",
        "transform = ax.transAxes\n",
        "line.set_transform(transform)\n",
        "ax.add_line(line)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x1700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "formattedRanges": [],
        "cell_id": "9f286787de684ed98473f570d6202d22",
        "deepnote_cell_type": "text-cell-h1",
        "id": "xHbPfahAId7B"
      },
      "source": [
        "# XGBOOST для данных без хвоста"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "a7cd426d",
        "execution_start": 1708806381927,
        "execution_millis": 187,
        "deepnote_to_be_reexecuted": true,
        "cell_id": "f03ea53a69704d5c8854ec83bc3b2af7",
        "deepnote_cell_type": "code",
        "id": "e_CoMGTzId7B"
      },
      "source": [
        "Xgb= halfdata.values[:,(0,1,2,3,4,5,6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)]\n",
        "Ygb= halfdata.values[:, 21]\n",
        "\n",
        "\n",
        "Xgb_train, Xgb_test, ygb_train, ygb_test = train_test_split(Xgb, Ygb, test_size=0.15, random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "2476cbc0",
        "execution_start": 1708806381935,
        "execution_millis": 1230,
        "deepnote_to_be_reexecuted": true,
        "cell_id": "b20921269a164821bfb225ced3b33e36",
        "deepnote_cell_type": "code",
        "id": "Rb_98-zsId7B"
      },
      "source": [
        "bst = XGBRegressor(n_estimators=100, max_depth=16, objective='reg:squarederror',eval_metric=\"rmse\")\n",
        "\n",
        "# fit model\n",
        "bst.fit(Xgb_train, ygb_train)\n",
        "\n",
        "predgb = bst.predict(Xgb_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "b623e53d",
        "execution_start": 1708806383180,
        "execution_millis": 72,
        "deepnote_to_be_reexecuted": true,
        "cell_id": "ee1d956bc605490a85c6e04fe0054e93",
        "deepnote_cell_type": "code",
        "id": "_-FgnKYNId7C"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "167f98f",
        "execution_start": 1708806383186,
        "execution_millis": 445,
        "deepnote_to_be_reexecuted": true,
        "cell_id": "ddbd3d49b7a34f09a2bd9f08dd6973ea",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iImJ8Z76Id7C",
        "outputId": "92b8182f-0ebf-4d57-f22c-fabec78b9325"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "\n",
        "rmse = mean_squared_error(ygb_test, predgb, squared=False)\n",
        "mae = mean_absolute_error(ygb_test, predgb)\n",
        "# Рассчет MAPE\n",
        "mape = np.mean((np.abs(ygb_test - predgb)) / (ygb_test)) * 100\n",
        "\n",
        "print(\"MAE:\", mae)\n",
        "print(\"MAPE:\", mape)\n",
        "print(\"RMSE:\", rmse)\n",
        "#44.70118629075763\n",
        "#42.76377150295522"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 3187.574861021008\n",
            "MAPE: inf\n",
            "RMSE: 5457.006404009666\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-439-b5a0fcffe6a7>:6: RuntimeWarning:\n",
            "\n",
            "divide by zero encountered in divide\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "source_hash": "d6b491dd",
        "execution_start": 1708806383204,
        "execution_millis": 428,
        "deepnote_to_be_reexecuted": true,
        "cell_id": "3a7355c2ecca4a61b36e35f95a03214c",
        "deepnote_cell_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "vh43yezpId7C",
        "outputId": "341d4ec2-6630-464e-d752-6fb416bdd240"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.lines as mlines\n",
        "import matplotlib.transforms as mtransforms\n",
        "\n",
        "x, y = np.random.random((2, 100))*2\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(predgb,ygb_test)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1700x1700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=bbb0613e-2f22-4925-8a20-4af8834a2a03' target=\"_blank\">\n",
        "<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='' > </img>\n",
        "Created in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>"
      ],
      "metadata": {
        "created_in_deepnote_cell": true,
        "deepnote_cell_type": "markdown",
        "id": "MQi8zudTId7C"
      }
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "deepnote_notebook_id": "ddceefdda9b149efb7889a6e3ec00963",
    "deepnote_execution_queue": [],
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "language_info": {
      "name": "python"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  }
}